current issues for research paper

Topics for Research Papers: Diverse Research Ideas Across Disciplines

Table of contents

• 1 Why Is the Choice of Subject in Research Papers So Important? 
• 2 How to Choose a Research Paper Topic?
• 3 Business Research Paper Topics
• 4 Media and Communications Research Paper Topics
• 5 Psychology Research Paper Topics
• 6 Argumentative Research Paper Topics
• 7 Controversial Topics for Research Paper
• 8 Easy Research Paper Topics
• 9 Research Topic in Natural Sciences
• 10 Sociology Research Paper Topics
• 11 Family Research Paper Topics
• 12 Discrimination Research Paper Topics
• 13 Law Research Paper Topics
• 14 Environmental Research Paper Topics
• 15 Education Research Paper Topics
• 16 Science and Technology Research Paper Topics
• 17 College Research Paper Topics
• 18 Health Research Paper Topics
• 19 Politics Research Paper Topics
• 20 History Research Paper Topics
• 21 Music Research Paper Topics
• 22.1 After Choosing the Research Topic
• 22.2 Closing Thoughts

When students approach the final stage of their academic journey, they immediately begin to think about the need to develop their research project to add to their college curriculum. To do this, students start searching for the best research paper topics. From then on, many insecurities and doubts will haunt the minds of these college education students who will have a big job ahead of them with the best research paper topic.

To help students during this challenging phase of their education, we can help them explore various interesting research paper topics, figure out which one aligns with their interests, and start working on it well ahead of the deadline.                    

Why Is the Choice of Subject in Research Papers So Important?  

The final project of a student who is in college is a research paper. However, the student needs to start working on it before the end of their program. It is known that the sooner you begin this process, the more time and tranquility you will find to develop it. Therefore, a research paper will review what experts on a given subject have published in the literature, making the student choose and defend a point of view concerning a thesis statement. Writing one successfully requires surveys and data to back your idea or topic and choose a good research paper topic.

How to Choose a Research Paper Topic?

Choosing a research paper topic is a crucial step in the research process . It’s important to select a topic that interests you, aligns with your academic goals, and is feasible to research. Here’s a step-by-step guide on how to pick a good research paper topic :

• Pick a subject that you’re passionate about! If you’re passionate about your subject, the research process will be more relevant, and you will have more research topic ideas.
• Concentrate on a doable topic.
• You will only be able to focus if your topic is narrow enough.
• Examine the subject selection instructions provided in your assignment—request advice from your lecturer or TA.
• Always refer to important materials and notes about classes so that you are up to date on the topic.
• Brainstorm research topics with a friend. They will be able to help you write your emphasis and raise concerns that will not be present at the time of your presentation.
• Consider the following questions for your research topic ideas: who, what, when, where, and why:

WHY did you choose this particular topic? What piques your curiosity about it?

WHO are the sources of information, and who is impacted?

WHAT are the most important questions about this subject?

WHERE does your topic matter most: locally, nationally, or internationally?

WHEN did your topic become important? Is this a current issue or one from the past?

Business Research Paper Topics

A study of the business is current and significant. It allows data and information to be collected about commercial relationships worldwide. In this way, students can delve deeper into an important economics and professional development topic, finding solutions and discussing results. Sometimes, one can get research paper writing to assist in situations where it is inconvenient. Some examples of these business research paper topics include:

• The influence of employee benefits on representative efficiency.
• The difficulties of managing workgroup conflict.
• Start-ups: Examining the challenges that arise during the early stages of a business.
• How to work with open and closed systems in business.
• Workgroups’ ability to evolve to meet the needs of the future.
• Inter-authoritative networks and leadership strategies.
• Product and service improvements made in significant cooperation.
• How to manage an organization’s emergencies.
• The effect of full quality management practices on client retention and satisfaction.
• Effective time management as a tool for authoritative endurance.

Media and Communications Research Paper Topics

The research work developed in the area of the media manages to provide students with information about mass communication and its importance. With the advent of the internet and all its nuances, it is increasingly important to develop studies and research to understand the effects of media and communication in the social context. Some communication research topics can explore include:

• The influence of the media on real-life relationships.
• The role of the media in the expression of social issues.
• Consequences of mass media on society’s ethical well-being.
• The advantages of using the media to anticipate a crisis.
• How the media infringes on citizens’ rights.
• What role does the media play in spreading awareness?
• The significance of trustworthy ads.
• The impact of the media on a country’s political elite.
• Is the media good or harmful to the economy?
• What role does the media play in technological advancement?

Psychology Research Paper Topics

Many subjects can be approached within the theme of psychology. It is an area of science that allows us to carry out different studies with various themes and views. However, the student must be able to choose an important topic for the psychology and academic context. Your teacher’s attention is drawn to an engaging and novel topic. Here are some ideas for you:

• What impact do violent video games have on children’s brain development?
• Do violent cartoons impact our children’s behavior and mental health?
• What impact does divorce have on a child’s emotional blooming?
• An introverted child’s social anxiety and sadness.
• How does bullying affect high school or college students’ social interactions?
• When a person is in love, how does the brain work?
• Differences in the way a man’s and a woman’s brains work
• Is there a link between PTSD and Alzheimer’s disease?
• Sleep paralysis causes and effects
• What is the best way to deal with multiple personality disorders?

Argumentative Research Paper Topics

The student must choose an argumentative research paper topic because you will have to defend a thesis during the work. By opting for a good research paper topic of your interest, you will base yourself on various materials already published on the subject to develop your thoughts and arguments to defend your initial idea chosen as the main subject of your work.

• The role of government in addressing income inequality.
• The pros and cons of school uniform policies.
• Analyzing the effects of violent video games on behavior.
• The effectiveness of abstinence-only sex education programs.
• The impact of fast food advertising on childhood obesity.
• The role of government in regulating online privacy.
• The pros and cons of school voucher programs.
• Examining the impact of standardized testing on education.
• The ethics of capital punishment for juveniles.
• The ethics of human organ trafficking.

Controversial Topics for Research Paper

• High times or high risks? The legalization of recreational marijuana use.
• Locked and loaded: The endless debate on gun control policies.
• To kill or not to kill: The ongoing debate on capital punishment.
• Drones in the crosshairs: Balancing security and privacy.
• The ethics of human cloning and genetic engineering.
• Vaccination controversies: Mandates, exemptions, and public health.
• Affirmative action under scrutiny: Equity in college admissions.
• The ethics of assisted suicide and euthanasia.
• Balancing surveillance and civil liberties.
• Nuclear power and weapons: Weighing the benefits and risks.
• Solitary confinement: Cruel punishment or necessary measure?
• Rise of the machines: Artificial intelligence, employment, and ethics.
• Animals in entertainment: Whose amusement at what cost?
• Privacy vs. security: Government surveillance and individual rights.
• Playing God: The ethics of human gene editing and designer babies.
• Statues and symbols: The controversy surrounding Confederate memorials.
• Fracking unveiled: The controversy surrounding hydraulic fracturing.

Easy Research Paper Topics

• The effects of fast food consumption on health.
• The benefits of reading for personal development.
• Strategies for stress reduction in everyday life.
• The role of family in child development.
• The history of famous inventions.
• Strategies for effective communication in relationships.
• The history and impact of social movements.
• The influence of advertising on consumer choices.
• Strategies for healthy eating on a budget.
• The importance of setting goals in life.

Research Topic in Natural Sciences

• The impact of ocean acidification on marine life
• Exploring the genetic diversity of soil microbes and their influence on crop health.
• Studying the impacts of emerging contaminants on freshwater ecosystems.
• Understanding the dynamics of emerging zoonotic diseases in wildlife populations.
• The use of CRISPR in gene editing.
• The use of drones in disaster response.
• The effects of light pollution on nocturnal insect communities in urban areas.
• Natural toxins in plants: mechanisms, benefits, and risks in agriculture.
• Climate change and its effects on algal blooms and water quality in 2023.
• The ecological significance of wetland restoration efforts in 2023.

Sociology Research Paper Topics

The sociology discipline is responsible for studying human behavior within society. These studies will address how people behave in social relationships and groups and how cultures and customs emerge. The possibilities for research are endless regarding sociology’s enormous reach and continually changing societal trends. Choose the best easy sociology research topics for yourself. Some topic ideas are:

• Is emotional intelligence as crucial as everyone claims?
• Is there a misconception that males are better professionals than women?
• Is homeschooling a hindrance to children’s socialization?
• Abuse marriages have both causes and effects.
• We spend too much time on our phones.
• The ethics of animal experimentation.
• Is recycling beneficial to the environment?
• In the United States, is there a stereotype about Mexicans?
• Racial preconceptions ruin people.
• The need for sex education.

Family Research Paper Topics

It’s not always easy to research family issues. This is mainly because some students may be coming from pleased families. Thus, many concepts might be difficult to comprehend. However, this field of study has the potential to significantly improve the psychological climate in many households throughout the world. Some topics one can research include:

• In the family, there has been sexual abuse. The escape plan and organizations that can assist.
• Resolving religious issues in families.
• Is it possible to rescue oneself from substance abuse in a family if others engage?
• Saving family connections while not inheriting the family business.
• Balancing between family support and allowing young adults to experience living on their own in the transition to adulthood.
• A relationship that is toxic and abusive. Breaking up with a toxic spouse has psychological ramifications.
• Children and adults go through phases of grieving after losing a family member. How can we cope as a group?
• What issues might parents confront when fostering a child?
• When does teen misbehavior become more than a normal desire for independence?
• A generational divide exists. The moral and cultural differences. Is this typical?

Discrimination Research Paper Topics

It is undeniable the massive impact that discrimination has in the society of the past and also of the present. Therefore, articles on discrimination and race relations must be the subject of academic research. Research papers on it can probe and proffer solutions through data found. Much research in this area can be developed, addressing relations of age, religion, race, sexual orientation, and any existing social conflict. Some topics to explore are:

• Colorism as a kind of discrimination.
• Discrimination Against Muslims in the Workplace.
• Discrimination Against Asian Americans in Colleges.
• Discrimination in the Workplace.
• Discrimination against people of color as a result of racial profiling.
• Discrimination Against Muslims in the Workplace is an issue that has to be addressed.
• Discrimination in the Workplace: How to Manage in an Inclusive Environment.
• Discrimination, social exclusion, and violence against LGBT people are issues that the LGBT community faces.
• Discrimination and Equality Struggle.
• Discrimination based on age in the workplace.

Law Research Paper Topics

Another fundamental form of research is legal research. It is a significant topic and one that allows us to have different approaches and points of view. Each country and each historical time will give us an even greater immersion in laws and legal conduct.

The law can be divided into 21 major areas. Just choose one of them to be the topic of your law research . Some areas of law are constitutional law, environmental law, and criminal law, among others. Some good topics include:

• What are the benefits of business rules for institutions and organizations?
• What is business law’s actual nature?
• What do we know about the classification of business rules and the legal regulation of non-consensual adoption in the United States?
• How does the law handle marriage and divorce for mentally ill people?
• The cultural influence of transgender marriage and its legal status in different countries.
• The environment and private property.
• Equal protection under the law and the principle of equality.
• The financial pyramid’s legal aspects.
• Environment-related crimes.
• Exchange Commission and Securities.

Environmental Research Paper Topics

To improve our relationship with nature and preserve it, it is necessary to carry out a lot of debate and research in this area. In this way, we will learn about the importance of ecology and the environment in the lives of all living beings. Developing work on this subject means containing the adverse effects of globalization, industrialization, and the use of nature. This way, we can save ecosystems, avoiding irreparable losses to our planet. Some environment research paper topics  are:

• Analysis and proposed remedies for lung cancer and radon.
• The negative impact of acid rain on aquatic life and natural resources.
• What can we do to protect wildlife from being killed by acid rain?
• What role did prehistoric creatures have in today’s ecosystems?
• The repercussion of air pollution and global warming on human health.
• Can recycling assist people worldwide in improving their health?
• What can be done to protect coral reefs from being destroyed?
• How much are garbage and pesticides to blame for our soil contamination?
• How big is the problem of ocean acidification?
• Permafrost melting and its impact on climate change.

Education Research Paper Topics

It is known that improving the educational system worldwide is imperative, better serving students and society. However, how can we do this? This type of research aims to make education more efficient and accessible for everyone. Some topics to consider are:

• Will one of the aims of modern education be the growth of emotional intelligence?
• Discrimination in education and its impact on students’ futures.
• Mentally ill persons need to be educated and socialized. There are some similarities and some variances.
• Education and censorship. Do we want to protect the pupils, or do we want to help them learn what they need to know?
• How do students in inclusive classrooms perform compared to students in non-inclusive classes?
• Is it still necessary to have grades in today’s education?
• Education and incentive: how to pique the interest of students.
• In what ways may modern education be compared to traditional schooling?
• Bullying and a negative psychological climate in the classroom. Its influence on educational quality.
• The importance of discipline in education and how it affects the learning process.

Science and Technology Research Paper Topics

We can see many positive technical advances in the world these days. However, some have negative consequences. Students must prioritize topics that influence many people’s lives and help develop technological research. At the same time, look at the advantages and preservation of technology in today’s world. Some technology topics include:

• What effect does alcohol intake have on athletes who are training?
• What influence does ADCC activity research have on HIV treatment?
• What function does CDfT-Cell Subsets play in HIV patient progression?
• What is the most significant technical breakthrough of the twentieth century?
• Is it possible for tech to ever replace human intelligence?
• What impact does tech play in genetic engineering’s advancement?
• Can microbial factories help solve the raw metals shortage?
• Is the paleo diet representative of ancient life? Is it the best diet for people?
• Is Europe’s objection to genetically modified crops motivated by science or economic considerations?
• Could molecular biology study into uncommon genetic abnormalities possibly lead to new insights into cancer and other diseases?

College Research Paper Topics

Developing research about the dilemmas faced daily by many students within the university environment is also extremely important. In this way, it becomes possible to analyze more objectively all social relations within the college, the influence of social media, the academic development of students, and especially the results that will be reaped in their profession in the future. The education system is an extensive topic with possibilities to develop easy research paper topics. In addition, it is an environment you are probably already familiar with, thus facilitating the data and information collection process. Some topics about it:

• The importance of higher education in a professional career for middle ages people.
• What are the main reasons that lead students to drop out of college?
• Why should people study foreign languages in college?
• The influence of the digital world on the university world.
• College and the financial hardship faced by many students.
• Doubts about choosing the right college.
• How to enter the job market after completing college.
• What are the main differences between public and private colleges?
• The effects of cyberbullying in the university context.
• What is the ideal human behaviour in the university environment?

Health Research Paper Topics

Academic work must be developed in Health subjects, seeking to understand better the dynamics of health in different world countries and how this influences people’s lives to have physical well-being. We have recently seen the impact of the Covid-19 virus pandemic on our planet. We have seen the collapse of many health systems in many countries’ human relationships. In addition, we also saw despair and suffering in many families with mental health problems. Therefore, we see how necessary it is to develop research projects that help consolidate better care for the population, promoting health and preventing the emergence of new diseases, especially in third-world countries. Some health research paper topics are:

• Support for family members who have lost loved ones to COVID-19.
• Adolescent violence as a public health problem.
• Assistance for people with disabilities living in rural areas.
• Adolescent mental health in public health.
• The evolution of the human body over the years.
• Health problems caused by the use of pesticides.
• How does social media addiction influence people’s health?
• Obesity as a public health problem.
• Health education in schools and through social media.
• How does water quality interfere with public health?

Politics Research Paper Topics

Another research paper topic is politics. Politics is a topic that directly or indirectly involves the lives of all world inhabitants. Through it, we seek our rights, have the strength to fight for our ideologies, and become citizens. Here are some politics research paper topics :

• Policies to face global warming.
• The presence of politics within the education system.
• The influences of the cultural industry in the constitution of public policies.
• Political relations during World War I and World War II.
• The importance of feminism in the political achievement of women’s right to vote.
• Best professional writers on political topics.
• Public policy related to an all-female working environment.
• The importance of public policies that make people aware of drug use.
• The emergence of extremist political ideologies in the 21st century.
• The importance of democracy in politics.

History Research Paper Topics

History is essential for humanity to understand the facts that occurred and their relationship with time. From history, we can develop several exciting research topics as it can investigate what men did, thought, felt, and set in all social dynamics thousands of years ago. Historical knowledge is capable of helping to understand human beings, their attitudes, and their evolution. Some history research paper topics are:

• The historical development of the electoral college.
• Major terrorist attacks throughout history.
• The historical and social importance of gender psychology.
• The changes that came with the end of the Soviet Union.
• The progression of the minimum wage over the years.
• The worldwide impacts of the war between Russia and Ukraine.
• The emergence of offshore bank accounts work and their social impact.
• The Mayan Civilization and Its Cultural Legacy.
• The history of dictatorships in South America and funding from the United States.
• The democratization of access to course material at History College.

Music Research Paper Topics

Music has been present in humanity since the most remote times. Through music, people can express themselves, have fun, relax, and express their opinions. As it is a vast topic, it is possible to do extensive research. Music also allows you to find funny research paper topics to develop. We selected a few good research paper topics here:

• The evolution of the music industry globally.
• The influence of music in the treatment of depression.
• The importance of still producing and listening to traditional music.
• The presence of music in film.
• The main legacies of classical music.
• Songs about self-esteem that encourage obese people.
• School development through music.
• The aid of artificial intelligence in the creation of new songs.
• The effects of music on brain development.
• Songs that address controversial topics and their importance.

Literature Research Paper Topics

Literature is everything that involves the art of the word. We consider literature an instrument capable of making us communicate and interact socially. With literature, we can transmit knowledge, emotions, and stories, and that’s why literature has many good research topics. Literature can provide us with the most interesting research topics. Here are some research paper ideas.

• Literature as a tool of corporate social responsibility.
• The first forms of literature found.
• The use of social media as a way to propagate literature.
• A sensitive approach to child abuse in the literature.
• Are people switching from literature to social media?
• The change of literature after the Industrial Revolution.
• Literature as a way to protect children online.
• The development of scientific literature for standardized tests in science.
• Literature review on computer science.
• The importance of developing a thesis statement in literature.

After Choosing the Research Topic

Well, after finding great research paper topics, you will decide which one you like the most and which you are more comfortable writing about. So, once you’ve decided which college research paper topics to cover in your thesis, it’s time to start writing. It is important, right at the beginning, to define your main research question to start researching websites, books, articles, etc. A tip is always to opt for a broader research question, so your first research will have a greater amount of material on your research topics, which will make it easier when choosing an approach and focusing on the main subject of your thesis. Before starting the process of submitting research papers, it is important that an advisor can read and review your text on the chosen research topics.

Closing Thoughts

When you have to choose your research paper topic, there are tons of ideas you can use to embark on your research journey. It is very important that you first carry out thorough research about a good research paper topic so that you can start writing your thesis statement. You can read materials online to know if the topic you want to write on is relevant and has enough data. I hope you can take advantage of all our tips to develop your research papers. The most important thing to consider is that you need to like the topic you choose for a good research paper because it’ll help make writing fun.

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Research Paper Topics - Best Picks

Did you know that more than 2.5 million research papers are published worldwide every year? This huge number highlights the vast array of topics students and researchers can explore. 

Below, you will find 200 interesting research topics to help spark your creativity and get you started on your next academic venture. Additionally, you can browse essay topics that can also help set your research in the right duration.

Top 10 Research Topics for Students

• Impact of ICT's (Information and Communication Technologies) on Society.
• Role-to-role networks: how Internet and mobile communications have changed the connectivity of social networks in few years.
• The future of electric vehicles and infrastructure: pros, cons and consequences.
• Ethical considerations in the development of autonomous weapons. Shall the World stop?
• Strategies to reduce economic inequality in developing countries.
• The impact of micro-plastics on marine ecosystems. Ways to safe Planet.
• New methods for detecting dark matter particles.
• The use of nano-materials in cancer treatment.
• The impact of vaccination programs on global health. Shall we win or be defeated?
• Studying the impact of solar storms on satellite communications.

Research Paper Topics on Math

• Applications of machine learning algorithms in data analysis and prediction.
• Mathematical modeling of biological systems and population dynamics.
• Cryptography and its applications in secure communication and data protection.
• Theoretical advances in number theory and their practical implications.
• Optimization techniques and their applications in engineering and operations research.
• Chaos theory and its applications in weather forecasting and complex systems.
• Graph theory and its applications in network analysis and social networks.
• Mathematical aspects of game theory and decision-making in economics.
• Topological methods in data analysis and pattern recognition.
• Fractal geometry and its applications in computer graphics and image compression.
• Statistical methods for analyzing large datasets and drawing meaningful conclusions.
• Algebraic geometry and its applications in computer vision and robotics.
• Pricing models for derivatives and risk management strategies.
• Computational methods for solving partial differential equations and fluid dynamics problems.
• Mathematical biology: Modeling biological processes and ecosystems.
• Discrete mathematics and its applications in computer science and information theory.
• Theoretical aspects of machine learning algorithms and their optimization.
• Mathematical logic and its role in computer science and artificial intelligence.
• Combinatorial optimization and its applications in logistics and resource allocation.
• Quantum computing: Mathematical foundations and potential applications in cryptography.

Research Paper Topics on Business

• The impact of remote work on organizational culture and productivity.
• Modern diversity and inclusion initiatives for businesses.
• Digital marketing strategies for small businesses in the post-pandemic era.
• The role of corporate social responsibility in enhancing brand reputation and consumer loyalty.
• Supply chain disruptions: Strategies for building resilience and mitigating risks.
• The rise of e-commerce and its implications for traditional brick-and-mortar businesses.
• Entrepreneurship and innovation for fostering a culture of creativity and growth.
• The future of retail: Trends and challenges in a digital-first world.
• Sustainable business practices: Balancing profitability with environmental responsibility.
• Leadership in times of crisis: Managing uncertainty and driving organizational change.
• Financial management strategies for small and medium-sized enterprises (SMEs).
• Employee engagement and retention: Strategies for cultivating a motivated workforce.
• Strategic partnerships and alliances: Leveraging collaboration for business success.
• Digital transformation: Adopting new technologies to stay competitive in the market.
• The gig economy: Implications for workforce management and talent acquisition.
• Business ethics and corporate governance: Principles for ethical decision-making and accountability.
• International expansion: Challenges and strategies for entering new markets.
• Customer relationship management: Building lasting connections in a digital world.
• Data-driven decision-making: Harnessing analytics for business growth and optimization.
• Crisis management and business continuity planning: Preparing for unexpected challenges.

Research Paper Topics on Psychology

• Psychological implications of social media use across different age groups and cultures.
• The psychology of risk-taking behavior: Understanding motivations and consequences.
• Cognitive biases and their impact on decision-making in various contexts.
• Psychological aspects of online learning: Motivation, engagement, and academic performance.
• The role of self-compassion in mental health and resilience.
• Exploring the psychology of conspiracy theories: Beliefs, motivations, and consequences.
• Cultural differences in emotion regulation and expression: Implications for mental health interventions.
• Psychological perspectives on environmental sustainability and pro-environmental behaviors.
• The psychology of procrastination: Underlying factors and strategies for change.
• Psychological effects of gaming and esports: Benefits, risks, and implications.
• The role of pets in human mental health and well-being.
• Psychological aspects of body image and eating behaviors across diverse populations.
• Exploring the psychology of humor: Effects on mood, social connections, and well-being.
• Psychological resilience in the face of natural disasters and traumatic events.
• The impact of color psychology on mood, behavior, and decision-making.
• Psychological factors influencing the adoption of sustainable lifestyle choices.
• The psychology of music: Emotional responses, preferences, and therapeutic applications.
• Understanding and addressing mental health stigma within healthcare systems.
• Psychological perspectives on aging and successful aging: Factors contributing to well-being in later life.
• The psychology of creativity in problem-solving and innovation across disciplines.

Research Paper Topics on Literature

• Love and loss in Shakespeare's sonnets.
• Gender representation in Jane Austen's novels.
• Mythology in Greek tragedies.
• Colonialism in post-colonial literature.
• "The Great Gatsby" and the American Dream.
• Identity in Toni Morrison's novels.
• Magical realism in García Márquez's "One Hundred Years of Solitude."
• War in Hemingway's novels.
• Alienation and existentialism in Kafka's works.
• Race and racism in African-American literature.
• Folklore and fairy tales in the Brothers Grimm's works.
• Nature in Romantic poetry.
• Political allegory in Orwell's "Animal Farm."
• Heroism in Homer's "The Odyssey."
• Satire in Swift's "Gulliver's Travels."
• Social class and inequality in Dickens' novels.
• Surrealism in García Lorca's poetry.
• Madness in Shakespeare's tragedies.
• Religion and morality in Dostoevsky's works.
• Modernity in the Harlem Renaissance literature.

Looking to Add a Dash of Humor to Your Academic Journey?

Well, why not order our collection of funny research paper topics and let the hilarity begin? Your grades will thank you later!

Research Paper Topics on Environment

• The interplay between urbanization and environmental sustainability.
• Technological innovations for addressing climate change challenges.
• Social, economic, and environmental implications of renewable energy transitions.
• Ecotourism as a tool for conservation and community development.
• Indigenous knowledge and practices in environmental management.
• The role of public policy in promoting sustainable development.
• Sustainable transportation solutions for reducing carbon emissions.
• The impact of consumer behavior on environmental sustainability.
• Green building technologies and their contribution to energy efficiency.
• The potential of circular economy models in waste reduction and resource conservation.
• Water scarcity and management strategies in arid regions.
• The nexus between agriculture, food security, and environmental sustainability.
• The role of citizen science in monitoring and addressing environmental issues.
• Environmental education and its influence on public awareness and behavior.
• Natural resource management and conflicts over land and water resources.
• The effects of pollution on ecosystems and human health.
• Indigenous rights and environmental conservation efforts.
• Environmental governance mechanisms at local, national, and international levels.
• The impact of globalization on natural resource exploitation and conservation.
• Resilience and adaptation strategies for communities vulnerable to environmental hazards.

Research Paper Topics on Biology

• The genetic basis of human diseases and potential therapeutic interventions.
• Evolutionary mechanisms driving biodiversity and speciation.
• Ecological impacts of climate change on species distribution and ecosystems.
• Cellular mechanisms underlying cancer development and progression.
• Genetic engineering and its applications in agriculture, medicine, and industry.
• Neurobiology of learning and memory formation in humans and animals.
• Biodiversity conservation strategies and their effectiveness in preserving ecosystems.
• Microbial communities and their role in human health and disease.
• Molecular mechanisms of aging and potential interventions for lifespan extension.
• Adaptations of extremophiles to extreme environmental conditions.
• Epigenetics and its role in gene expression regulation and disease susceptibility.
• The immune system and its response to pathogens and vaccines.
• Plant-microbe interactions and their impact on plant health and productivity.
• Population genetics and its implications for conservation biology and evolutionary studies.
• Genetic basis of behavior and its evolutionary significance.
• Biotechnology applications in environmental remediation and pollution control.
• Comparative genomics and evolutionary relationships among different species.
• Physiological adaptations of organisms to changing environmental conditions.
• Bioinformatics tools and their role in analyzing genomic and proteomic data.
• Ethical considerations in biotechnological research and applications.

Research Paper Topics on Health and Medicine

• The global impact of telemedicine on healthcare access and patient outcomes.
• Genetic advancements and their role in personalized medicine worldwide.
• Mental health awareness initiatives and their effectiveness across cultures.
• The influence of dietary patterns on chronic disease management in different populations.
• Comparative analysis of universal healthcare systems in various countries.
• The role of vaccines in preventing global infectious diseases.
• Physical activity's effects on mental health and well-being in diverse populations.
• The effectiveness of alternative medicine in various cultural settings.
• Socioeconomic disparities and their impact on global healthcare access.
• International strategies for combating antibiotic resistance.
• Public health campaigns and their success in reducing lifestyle-related diseases globally.
• Environmental determinants of health outcomes in different regions.
• Mental health intervention effectiveness in multicultural contexts.
• Innovations in cancer treatment and their global impact on survival rates.
• Emerging medical technologies and their role in enhancing surgical outcomes.
• The effects of health policies on managing non-communicable diseases worldwide.
• The challenges and opportunities of aging populations on healthcare systems globally.
• Primary care's role in chronic disease management across various healthcare systems.
• Global perspectives on stress management techniques and health outcomes.
• International health initiatives and their success in reducing global health disparities.

Research Paper Topics on Education

• The impact of technology on education systems globally.
• Teacher-student relationships and their effect on academic success.
• The influence of standardized testing on student learning and motivation.
• Innovative strategies for improving literacy rates in various educational settings.
• Benefits and challenges of inclusive education for students with diverse needs.
• The impact of parental involvement on student academic achievement and behavior.
• The role of arts education in fostering creativity and critical thinking.
• Comparing the effectiveness of online learning and traditional classroom education.
• Socioeconomic status and its influence on educational opportunities and outcomes.
• The impact of school leadership on teacher performance and student success.
• Benefits of bilingual education programs in diverse communities.
• The role of extracurricular activities in student development and academic performance.
• Long-term academic benefits of early childhood education programs.
• Strategies for closing the achievement gap in different types of schools.
• Effects of classroom environment on student learning and engagement.
• The role of vocational education in preparing students for various career paths.
• School policies' impact on bullying prevention and student safety.
• Importance of teacher professional development for enhancing instructional quality.
• Cultural diversity's influence on teaching practices and student learning.
• The role of educational technology in supporting personalized and adaptive learning.

Research Paper Topics on Sports

• Mental health benefits of regular sports participation.
• The role of proper nutrition in enhancing athletic performance.
• Gender differences in sports performance and participation rates.
• The ethical and health implications of doping in competitive sports.
• How different coaching styles influence team success and player development.
• The relationship between adequate sleep and peak athletic performance.
• The impact of modern technology on sports training and athlete performance.
• The physical and social benefits of participation in youth sports programs.
• The role of sports in building stronger, more cohesive communities.
• Effects of climate and weather conditions on outdoor sports performance.
• The importance of injury prevention strategies in competitive sports.
• Psychological effects of winning and losing on athletes.
• The evolution and impact of sports equipment design on performance.
• The role of regular sports participation in promoting overall physical fitness.
• The influence of media coverage on public perception of professional athletes.
• Historical development and significance of the Olympic Games.
• Economic impacts of hosting major sports events on local communities.
• Unique challenges faced by female athletes in male-dominated sports.
• The effect of crowd support on the performance of athletes and teams.
• The relationship between participation in sports and academic performance in students.

Seven Signs of Good Topics for Research Paper

Here are the main characteristics of engaging research paper ideas:

If you don’t feel that your topic meets the abovementioned criteria and the time is too short to brainstorm more research topic ideas, simply say, ‘ write my paper ,’ and one of our experts will be there to help you shortly.

How to Choose a Quality Research Paper Topic

Always select a topic that genuinely interests you and aligns with your passion. Your enthusiasm will fuel your motivation and commitment throughout the research process.

Stay Current

Choose a relevant and timely topic. Look for emerging trends, pressing issues, or gaps in the literature that your research can address. Consult this guide if you need more information about how to research a topic .

Consider Your Audience

Think about who your audience is and what topics would be most engaging and relevant to them. Tailor your topic selection to the interests and needs of your audience.

Be Specific

Narrow down your focus to a specific research question or problem. This will help you avoid tackling overly broad topics and ensure your research is focused and manageable.

Assess Feasibility

Consider the resources, time, and access to data or information available for your research. Choose a topic that you can feasibly research within the constraints of your academic program or timeline.

Seek Inspiration

Look for inspiration from miscellaneous sources, including academic journals, news articles, conferences, and conversations with peers or mentors. Explore different perspectives and ideas to spark your creativity.

Gather Feedback

Discuss your potential topics with your peers, instructors, or academic advisors. They can provide valuable feedback, offer different perspectives, and help you refine your research paper ideas. If you want to join forces with a subject-relevant professor, use our research proposal writing service to entice them to work with you.

Access Significance

Measure the significance of your chosen topic in the context of your field of study. Consider its relevance, potential contributions to existing knowledge, and implications for theory, practice, or policy.

Remain Flexible

Be open to adjusting your research topic ideas as you conduct further analysis and gain new insights. Research is an iterative process, and your topic may evolve as you delve deeper into the literature and data.

Stay True to Yourself

Choose a topic that resonates with you and reflects your academic interests, values, and goals. Your research paper is an opportunity to contribute meaningfully to your field and showcase your expertise and passion. If that doesn’t help, buy a research paper from our writing specialists to save time and nerves.

Students often find it difficult to generate research paper topic ideas due to factors such as a lack of clarity on their interests, limited access to relevant resources, and time constraints. 

A solution to this challenge is to consider utilizing a professional writing service, which can provide expert guidance and assistance in selecting a research paper topic tailored to the student's interests and requirements. 

To bring your academic writing prowess to the next level, please consider the following guide on how to write a reflective essay .

Looking for A+ Argumentative Research Paper Topics?

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What Are 5 Good Research Questions for Students?

What are the best research topics, what topic should i use for my research paper.

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is a seasoned educational writer focusing on scholarship guidance, research papers, and various forms of academic essays including reflective and narrative essays. His expertise also extends to detailed case studies. A scholar with a background in English Literature and Education, Daniel’s work on EssayPro blog aims to support students in achieving academic excellence and securing scholarships. His hobbies include reading classic literature and participating in academic forums.

is an expert in nursing and healthcare, with a strong background in history, law, and literature. Holding advanced degrees in nursing and public health, his analytical approach and comprehensive knowledge help students navigate complex topics. On EssayPro blog, Adam provides insightful articles on everything from historical analysis to the intricacies of healthcare policies. In his downtime, he enjoys historical documentaries and volunteering at local clinics.

• Added infographics.
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• Research Paper Topics -Ijser. (n.d.). https://www.ijser.org/research-paper-topics.aspx ‍
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Look at the "Picking a Topic" tab on this guide for help brainstorming your topic. Also, our Research Process guide can help you throughout your research process.

• Research Process by Liz Svoboda Last Updated May 29, 2024 10854 views this year

• Affirmative Action
• Affordable Care Act
• Alternative medicine
• America's global influence
• Artificial intelligence
• Assisted suicide
• Bilingual education
• Black Lives Matter
• Border security
• Capital punishment
• Charter schools
• Childhood obesity
• Civil rights
• Climate change
• Concussions in football
• COVID restrictions
• Cryptocurrency
• Cyber bullying
• Cybersecurity
• Drug legalization
• Early voting
• Eating disorders
• Equal Rights Amendment
• Executive order
• Factory farming
• Foreign aid
• Freedom of speech
• General Data Protection Regulation
• Genetic engineering
• Gerrymandering
• Green New Deal
• Hate speech
• Health insurance
• Human trafficking
• Immigration
• Israel-Palestine relations
• Judicial activism
• Labor unions
• Land acknowledgments
• #MeToo movement
• Minimum wage
• Misinformation
• Net neutrality
• Nuclear energy
• Offshore drilling
• Online anonymity
• Organic food
• Outsourcing
• Police reform
• Political activism
• Prescription drug addiction
• Racial profiling
• Reparations
• Russian hacking
• Sanctuary city
• Screen addiction
• Self-driving cars
• Sex education
• Smart speakers
• Social Security reform
• Standardized testing
• Stimulus packages
• Supreme Court confirmation
• Syrian civil war
• Title IX enforcement
• Trade tariffs
• Transgender rights
• Ukraine and Russia
• Urban agriculture
• Vaccination mandates
• Violence in the media
• Voter ID laws
• Voting fraud and security
• White nationalism
• Women's rights
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113 Great Research Paper Topics

General Education

One of the hardest parts of writing a research paper can be just finding a good topic to write about. Fortunately we've done the hard work for you and have compiled a list of 113 interesting research paper topics. They've been organized into ten categories and cover a wide range of subjects so you can easily find the best topic for you.

In addition to the list of good research topics, we've included advice on what makes a good research paper topic and how you can use your topic to start writing a great paper.

What Makes a Good Research Paper Topic?

Not all research paper topics are created equal, and you want to make sure you choose a great topic before you start writing. Below are the three most important factors to consider to make sure you choose the best research paper topics.

#1: It's Something You're Interested In

A paper is always easier to write if you're interested in the topic, and you'll be more motivated to do in-depth research and write a paper that really covers the entire subject. Even if a certain research paper topic is getting a lot of buzz right now or other people seem interested in writing about it, don't feel tempted to make it your topic unless you genuinely have some sort of interest in it as well.

#2: There's Enough Information to Write a Paper

Even if you come up with the absolute best research paper topic and you're so excited to write about it, you won't be able to produce a good paper if there isn't enough research about the topic. This can happen for very specific or specialized topics, as well as topics that are too new to have enough research done on them at the moment. Easy research paper topics will always be topics with enough information to write a full-length paper.

Trying to write a research paper on a topic that doesn't have much research on it is incredibly hard, so before you decide on a topic, do a bit of preliminary searching and make sure you'll have all the information you need to write your paper.

#3: It Fits Your Teacher's Guidelines

Don't get so carried away looking at lists of research paper topics that you forget any requirements or restrictions your teacher may have put on research topic ideas. If you're writing a research paper on a health-related topic, deciding to write about the impact of rap on the music scene probably won't be allowed, but there may be some sort of leeway. For example, if you're really interested in current events but your teacher wants you to write a research paper on a history topic, you may be able to choose a topic that fits both categories, like exploring the relationship between the US and North Korea. No matter what, always get your research paper topic approved by your teacher first before you begin writing.

113 Good Research Paper Topics

Below are 113 good research topics to help you get you started on your paper. We've organized them into ten categories to make it easier to find the type of research paper topics you're looking for.

Arts/Culture

• Discuss the main differences in art from the Italian Renaissance and the Northern Renaissance .
• Analyze the impact a famous artist had on the world.
• How is sexism portrayed in different types of media (music, film, video games, etc.)? Has the amount/type of sexism changed over the years?
• How has the music of slaves brought over from Africa shaped modern American music?
• How has rap music evolved in the past decade?
• How has the portrayal of minorities in the media changed?

Current Events

• What have been the impacts of China's one child policy?
• How have the goals of feminists changed over the decades?
• How has the Trump presidency changed international relations?
• Analyze the history of the relationship between the United States and North Korea.
• What factors contributed to the current decline in the rate of unemployment?
• What have been the impacts of states which have increased their minimum wage?
• How do US immigration laws compare to immigration laws of other countries?
• How have the US's immigration laws changed in the past few years/decades?
• How has the Black Lives Matter movement affected discussions and view about racism in the US?
• What impact has the Affordable Care Act had on healthcare in the US?
• What factors contributed to the UK deciding to leave the EU (Brexit)?
• What factors contributed to China becoming an economic power?
• Discuss the history of Bitcoin or other cryptocurrencies  (some of which tokenize the S&P 500 Index on the blockchain) .
• Do students in schools that eliminate grades do better in college and their careers?
• Do students from wealthier backgrounds score higher on standardized tests?
• Do students who receive free meals at school get higher grades compared to when they weren't receiving a free meal?
• Do students who attend charter schools score higher on standardized tests than students in public schools?
• Do students learn better in same-sex classrooms?
• How does giving each student access to an iPad or laptop affect their studies?
• What are the benefits and drawbacks of the Montessori Method ?
• Do children who attend preschool do better in school later on?
• What was the impact of the No Child Left Behind act?
• How does the US education system compare to education systems in other countries?
• What impact does mandatory physical education classes have on students' health?
• Which methods are most effective at reducing bullying in schools?
• Do homeschoolers who attend college do as well as students who attended traditional schools?
• Does offering tenure increase or decrease quality of teaching?
• How does college debt affect future life choices of students?
• Should graduate students be able to form unions?

• What are different ways to lower gun-related deaths in the US?
• How and why have divorce rates changed over time?
• Is affirmative action still necessary in education and/or the workplace?
• Should physician-assisted suicide be legal?
• How has stem cell research impacted the medical field?
• How can human trafficking be reduced in the United States/world?
• Should people be able to donate organs in exchange for money?
• Which types of juvenile punishment have proven most effective at preventing future crimes?
• Has the increase in US airport security made passengers safer?
• Analyze the immigration policies of certain countries and how they are similar and different from one another.
• Several states have legalized recreational marijuana. What positive and negative impacts have they experienced as a result?
• Do tariffs increase the number of domestic jobs?
• Which prison reforms have proven most effective?
• Should governments be able to censor certain information on the internet?
• Which methods/programs have been most effective at reducing teen pregnancy?
• What are the benefits and drawbacks of the Keto diet?
• How effective are different exercise regimes for losing weight and maintaining weight loss?
• How do the healthcare plans of various countries differ from each other?
• What are the most effective ways to treat depression ?
• What are the pros and cons of genetically modified foods?
• Which methods are most effective for improving memory?
• What can be done to lower healthcare costs in the US?
• What factors contributed to the current opioid crisis?
• Analyze the history and impact of the HIV/AIDS epidemic .
• Are low-carbohydrate or low-fat diets more effective for weight loss?
• How much exercise should the average adult be getting each week?
• Which methods are most effective to get parents to vaccinate their children?
• What are the pros and cons of clean needle programs?
• How does stress affect the body?
• Discuss the history of the conflict between Israel and the Palestinians.
• What were the causes and effects of the Salem Witch Trials?
• Who was responsible for the Iran-Contra situation?
• How has New Orleans and the government's response to natural disasters changed since Hurricane Katrina?
• What events led to the fall of the Roman Empire?
• What were the impacts of British rule in India ?
• Was the atomic bombing of Hiroshima and Nagasaki necessary?
• What were the successes and failures of the women's suffrage movement in the United States?
• What were the causes of the Civil War?
• How did Abraham Lincoln's assassination impact the country and reconstruction after the Civil War?
• Which factors contributed to the colonies winning the American Revolution?
• What caused Hitler's rise to power?
• Discuss how a specific invention impacted history.
• What led to Cleopatra's fall as ruler of Egypt?
• How has Japan changed and evolved over the centuries?
• What were the causes of the Rwandan genocide ?

• Why did Martin Luther decide to split with the Catholic Church?
• Analyze the history and impact of a well-known cult (Jonestown, Manson family, etc.)
• How did the sexual abuse scandal impact how people view the Catholic Church?
• How has the Catholic church's power changed over the past decades/centuries?
• What are the causes behind the rise in atheism/ agnosticism in the United States?
• What were the influences in Siddhartha's life resulted in him becoming the Buddha?
• How has media portrayal of Islam/Muslims changed since September 11th?

Science/Environment

• How has the earth's climate changed in the past few decades?
• How has the use and elimination of DDT affected bird populations in the US?
• Analyze how the number and severity of natural disasters have increased in the past few decades.
• Analyze deforestation rates in a certain area or globally over a period of time.
• How have past oil spills changed regulations and cleanup methods?
• How has the Flint water crisis changed water regulation safety?
• What are the pros and cons of fracking?
• What impact has the Paris Climate Agreement had so far?
• What have NASA's biggest successes and failures been?
• How can we improve access to clean water around the world?
• Does ecotourism actually have a positive impact on the environment?
• Should the US rely on nuclear energy more?
• What can be done to save amphibian species currently at risk of extinction?
• What impact has climate change had on coral reefs?
• How are black holes created?
• Are teens who spend more time on social media more likely to suffer anxiety and/or depression?
• How will the loss of net neutrality affect internet users?
• Analyze the history and progress of self-driving vehicles.
• How has the use of drones changed surveillance and warfare methods?
• Has social media made people more or less connected?
• What progress has currently been made with artificial intelligence ?
• Do smartphones increase or decrease workplace productivity?
• What are the most effective ways to use technology in the classroom?
• How is Google search affecting our intelligence?
• When is the best age for a child to begin owning a smartphone?
• Has frequent texting reduced teen literacy rates?

How to Write a Great Research Paper

Even great research paper topics won't give you a great research paper if you don't hone your topic before and during the writing process. Follow these three tips to turn good research paper topics into great papers.

#1: Figure Out Your Thesis Early

Before you start writing a single word of your paper, you first need to know what your thesis will be. Your thesis is a statement that explains what you intend to prove/show in your paper. Every sentence in your research paper will relate back to your thesis, so you don't want to start writing without it!

As some examples, if you're writing a research paper on if students learn better in same-sex classrooms, your thesis might be "Research has shown that elementary-age students in same-sex classrooms score higher on standardized tests and report feeling more comfortable in the classroom."

If you're writing a paper on the causes of the Civil War, your thesis might be "While the dispute between the North and South over slavery is the most well-known cause of the Civil War, other key causes include differences in the economies of the North and South, states' rights, and territorial expansion."

#2: Back Every Statement Up With Research

Remember, this is a research paper you're writing, so you'll need to use lots of research to make your points. Every statement you give must be backed up with research, properly cited the way your teacher requested. You're allowed to include opinions of your own, but they must also be supported by the research you give.

#3: Do Your Research Before You Begin Writing

You don't want to start writing your research paper and then learn that there isn't enough research to back up the points you're making, or, even worse, that the research contradicts the points you're trying to make!

Get most of your research on your good research topics done before you begin writing. Then use the research you've collected to create a rough outline of what your paper will cover and the key points you're going to make. This will help keep your paper clear and organized, and it'll ensure you have enough research to produce a strong paper.

What's Next?

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Christine graduated from Michigan State University with degrees in Environmental Biology and Geography and received her Master's from Duke University. In high school she scored in the 99th percentile on the SAT and was named a National Merit Finalist. She has taught English and biology in several countries.

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Food for Thought

When writing on controversial issues, exercise caution when considering a topic that has the potential to be overdone.  Issues such as gun control, abortion, corporal punishment, etc. have been written about so much for so long that it may be difficult to find something new to add to the conversation. Here are a few databases that have extensive browse for topic features:

Issues and Controversies is the place to go to prepare for a debate, discussion, research paper, or persuasive writing assignment. This database explores more than 800 hot topics in business, politics, government, education, and popular culture.

Learn more about Issues and Controversies using the resources below:

Search Tips

View our OneFile Research Guide to learn more about navigating GALE databases. 

More Places to Search

Explore our General Resources for Research: Multidisciplinary Databases research guide for additional resources. 

Opposing Viewpoints in Context is a rich resource for debaters and includes viewpoints, reference articles, infographics, news, images, video, audio, and more. This database centers on the key social issues of our time.

Learn more about Opposing Viewpoints in Context using the resources below:

View our tip sheet for information on how to locate materials in this database. View our OneFile Research Guide to learn more about navigating GALE databases. 

Points of View Reference Center contains many topics, each with an overview (objective background/description), point (argument) and counterpoint (opposing argument). For each topic, this database also offers a Guide to Critical Analysis, which helps the reader evaluate the controversial topics.

View our tip sheet and our YouTube tutorial for information on how to locate materials in this database. View our EBSCO Research Guide to learn more about navigating EBSCO databases. 

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Teachers’ resilience scale for sustainability enabled by ict/metaverse learning technologies: factorial structure, reliability, and validation.

1. Introduction

2. methodology, 2.1. background of subjects and procedure, 2.2. instrument and measures, 2.3. type of analysis, 3.1. constructing and validating the teacher’s ict/meefs resilience scale, 3.2. multiple regression analysis, 4. discussion, 5. conclusions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Makrakis, V. Teachers’ Resilience Scale for Sustainability Enabled by ICT/Metaverse Learning Technologies: Factorial Structure, Reliability, and Validation. Sustainability 2024 , 16 , 7679. https://doi.org/10.3390/su16177679

Makrakis V. Teachers’ Resilience Scale for Sustainability Enabled by ICT/Metaverse Learning Technologies: Factorial Structure, Reliability, and Validation. Sustainability . 2024; 16(17):7679. https://doi.org/10.3390/su16177679

Makrakis, Vassilios. 2024. "Teachers’ Resilience Scale for Sustainability Enabled by ICT/Metaverse Learning Technologies: Factorial Structure, Reliability, and Validation" Sustainability 16, no. 17: 7679. https://doi.org/10.3390/su16177679

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• How to Define a Research Problem | Ideas & Examples

How to Define a Research Problem | Ideas & Examples

Published on November 2, 2022 by Shona McCombes and Tegan George. Revised on May 31, 2023.

A research problem is a specific issue or gap in existing knowledge that you aim to address in your research. You may choose to look for practical problems aimed at contributing to change, or theoretical problems aimed at expanding knowledge.

Some research will do both of these things, but usually the research problem focuses on one or the other. The type of research problem you choose depends on your broad topic of interest and the type of research you think will fit best.

This article helps you identify and refine a research problem. When writing your research proposal or introduction , formulate it as a problem statement and/or research questions .

Table of contents

Why is the research problem important, step 1: identify a broad problem area, step 2: learn more about the problem, other interesting articles, frequently asked questions about research problems.

Having an interesting topic isn’t a strong enough basis for academic research. Without a well-defined research problem, you are likely to end up with an unfocused and unmanageable project.

You might end up repeating what other people have already said, trying to say too much, or doing research without a clear purpose and justification. You need a clear problem in order to do research that contributes new and relevant insights.

Whether you’re planning your thesis , starting a research paper , or writing a research proposal , the research problem is the first step towards knowing exactly what you’ll do and why.

Prevent plagiarism. Run a free check.

As you read about your topic, look for under-explored aspects or areas of concern, conflict, or controversy. Your goal is to find a gap that your research project can fill.

Practical research problems

If you are doing practical research, you can identify a problem by reading reports, following up on previous research, or talking to people who work in the relevant field or organization. You might look for:

• Issues with performance or efficiency
• Processes that could be improved
• Areas of concern among practitioners
• Difficulties faced by specific groups of people

Examples of practical research problems

Voter turnout in New England has been decreasing, in contrast to the rest of the country.

The HR department of a local chain of restaurants has a high staff turnover rate.

A non-profit organization faces a funding gap that means some of its programs will have to be cut.

Theoretical research problems

If you are doing theoretical research, you can identify a research problem by reading existing research, theory, and debates on your topic to find a gap in what is currently known about it. You might look for:

• A phenomenon or context that has not been closely studied
• A contradiction between two or more perspectives
• A situation or relationship that is not well understood
• A troubling question that has yet to be resolved

Examples of theoretical research problems

The effects of long-term Vitamin D deficiency on cardiovascular health are not well understood.

The relationship between gender, race, and income inequality has yet to be closely studied in the context of the millennial gig economy.

Historians of Scottish nationalism disagree about the role of the British Empire in the development of Scotland’s national identity.

Next, you have to find out what is already known about the problem, and pinpoint the exact aspect that your research will address.

Context and background

• Who does the problem affect?
• Is it a newly-discovered problem, or a well-established one?
• What research has already been done?
• What, if any, solutions have been proposed?
• What are the current debates about the problem? What is missing from these debates?

Specificity and relevance

• What particular place, time, and/or group of people will you focus on?
• What aspects will you not be able to tackle?
• What will the consequences be if the problem is not resolved?

Example of a specific research problem

A local non-profit organization focused on alleviating food insecurity has always fundraised from its existing support base. It lacks understanding of how best to target potential new donors. To be able to continue its work, the organization requires research into more effective fundraising strategies.

Once you have narrowed down your research problem, the next step is to formulate a problem statement , as well as your research questions or hypotheses .

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Software product line testing: a systematic literature review

• Open access
• Published: 02 September 2024
• Volume 29 , article number  146 , ( 2024 )

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• Halimeh Agh   ORCID: orcid.org/0000-0003-0272-9092 1 ,
• Aidin Azamnouri 1 &
• Stefan Wagner 1 , 2  

A Software Product Line (SPL) is a software development paradigm in which a family of software products shares a set of core assets. Testing has a vital role in both single-system development and SPL development in identifying potential faults by examining the behavior of a product or products, but it is especially challenging in SPL. There have been many research contributions in the SPL testing field; therefore, assessing the current state of research and practice is necessary to understand the progress in testing practices and to identify the gap between required techniques and existing approaches. This paper aims to survey existing research on SPL testing to provide researchers and practitioners with up-to-date evidence and issues that enable further development of the field. To this end, we conducted a Systematic Literature Review (SLR) with seven research questions in which we identified and analyzed 118 studies dating from 2003 to 2022. The results indicate that the literature proposes many techniques for specific aspects (e.g., controlling cost/effort in SPL testing); however, other elements (e.g., regression testing and non-functional testing) still need to be covered by existing research. Furthermore, most approaches are evaluated by only one empirical method, most of which are academic evaluations. This may jeopardize the adoption of approaches in industry. The results of this study can help identify gaps in SPL testing since specific points of SPL Engineering still need to be addressed entirely.

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1 Introduction

Software Product Line (SPL) engineering has proven to be an efficient and effective strategy to decrease implementation costs, reduce time to market, and improve the quality of derived products (Denger and Kolb 2006 ; Northrop et al. 2007 ). SPLs and Configurable Systems (Alves Pereira et al. 2020 ) are two approaches used in software engineering to manage and create software with varying levels of customization and flexibility. While both SPLs and configurable systems share the goal of offering flexibility and customization, they differ in their core approach. SPLs primarily emphasize the systematic reuse of components, architectures, and design patterns across a range of related software products. In contrast, configurable systems are single software products designed to be adaptable, enabling users to configure them to meet their unique requirements. We decided to limit the scope on SPL to keep the review focused.

Testing is an essential part of SPL Engineering (SPLE) to identify potential faults (Pohl and Metzger 2006 ). This activity examines core assets shared among many products, product-specific parts, and the interaction among them (McGregor 2001 ). Therefore, SPL testing includes activities from the validation of initial requirements to the acceptance testing of a specific product by customers (Da Mota Silveira Neto et al. 2011 ).

As the adoption of the SPL approach by companies has grown (Weiss 2008 ), many researchers have made contributions in the SPL testing field to provide efficient and effective approaches that can satisfy specific needs of the industry (e.g., controlling the cost/effort of SPL testing). This resulted in many publications on different aspects of SPL testing. Therefore, analyzing research conducted in this field using well-known empirical methods is required to provide an overview of state-of-the-art testing practices and assess the effectiveness of the proposed approaches. To this end, Systematic Literature Reviews (SLR) and Systematic Mapping Studies (SMS) were conducted on SPL testing, but the most recent one dates back to 2014 (do Carmo Machado et al. 2014 ). While some recent research has focused on reviewing specific aspects of SPL testing, such as model-based testing of SPLs (Petry et al. 2020 ), test case prioritization for SPL (Kumar 2016 ), and combinatorial interaction testing for software product lines (Lopez-Herrejon et al. 2015 ), there has not been an SLR or SMS since 2014 that provides a comprehensive overview of the current state of SPL testing in a general context. Therefore, there is a need to update existing literature reviews (Mendes et al. 2020 ) to identify up-to-date evidence and issues that enable further development of the SPL testing field.

This paper presents an SLR to analyze interesting aspects of SPL testing that are formalized as research questions. An SLR is a rigorous and systematic method to identify, evaluate, and interpret all available research relevant to a particular research question, topic area, or phenomenon of interest (Cruzes and Dybä 2011 ). The specific aspects based on which we analyzed relevant studies are:

Characteristics of the studies focused on SPL testing.

Test levels executed throughout the SPL lifecycle.

Creating test assets by considering commonalities and variabilities.

Dealing with configuration-aware software testing.

Preserving traceability between test assets and other artifacts.

Testing non-functional requirements in an SPL.

Controlling cost/effort of SPL testing.

The SLR process was conducted from June 2022 to the end of 2022. While some of the findings derived from this SLR align with the conclusions of previous SLRs, such as the identification of existing gaps in non-functional testing for SPLs and the necessity for more robust and user-friendly testing tools, our review uncovered specific insights and unaddressed gaps in this domain that were not fully explored in prior SLRs. These include:

Variability control, referring to the disciplined management and regulation of feature variations within SPLs, alongside modeling and tracing, presents persistent challenges that require attention throughout the testing process. Variability control involves implementing strategies, such as configuration and change management, to ensure consistency and predictability in the diverse configurations of products derived from the SPL.

Novel approaches are needed for regression test selection, prioritization, and minimization, along with architecture-based regression testing, to effectively manage regression testing in SPLs.

Promoting the adoption of SPL testing practices in industrial settings necessitates addressing practical challenges, such as offering guidance for industry-specific SPL testing, and conducting industrial evaluations.

Exploring the details of test levels across the SPL lifecycle and highlighting the consequences of neglecting a particular test level can offer valuable insights for practitioners.

Studies focusing on testing SPLs rarely address traceability explicitly. Considering feature variability and configuration management, more efficient methods for modeling and representing traceability relationships are required.

The remainder of this paper is organized as follows: Sect.  2 provides background information required to understand SPL and SPL testing concepts; Sect.  3 describes how the SLR methodology has been applied; the results of the SLR are reported in Sect.  4 ; potential threats to the validity of this study and the strategies employed to mitigate them are discussed in Sect. 5 ; Sect.  6 presents a summary of the research and examines the main findings; Sect.  7 provides a survey of the related research; Sect.  8 presents concluding remarks and further research.

2 Background

This section provides a concise background on the SPL development process, variability management, and testing approaches and levels as a basis for the remainder of this article.

2.1 SPL development process

SPL is a software development paradigm to achieve economies of scale and scope by analyzing product commonalities and variabilities. As this paradigm has specific benefits such as substantial cost savings, reduction of time to market, and high productivity, many organizations, including Philips, Nokia, Cummins, and Hewlett-Packard, have adopted it (Clements and Northrop 2002 ). In SPL, a set of core assets (e.g., reference architecture and reusable components) is first developed. Specific products are then built by configuring and composing the core assets in a prescribed way with product-specific features to satisfy particular market segments (Clements and Northrop 2002 ).

The SPL development process/lifecycle can be divided into two distinct phases: Domain Engineering and Application Engineering. According to Czarnecki and Eisenecker ( 2000 , p. 20), Domain Engineering is “the activity of collecting, organizing, and storing experience in building systems or parts of systems in a particular domain in the form of reusable assets, as well as providing an adequate means for reusing these assets when building new systems.” Application Engineering is focused on deriving specific products from the core assets created during Domain Engineering; in this phase, specifics of the products are added to common parts to satisfy the particular needs of a product (Clements and Northrop 2002 ). Of these two phases, Domain Engineering demands significant resources and time. If not managed effectively, it can lead to the failure of the entire SPL (Pohl et al. 2005 , p. 9–10). Three common approaches are employed for constructing an SPL, and each of these approaches directly influences the implementation of Domain Engineering (Apel et al. 2013 ):

Proactive approaches start with a comprehensive and thorough scoping of the domain to anticipate all requirements. Subsequently, all these requirements are implemented as assets, and SPL experts typically carry out this task.

Extractive approaches follow an automated process, utilizing a set of existing product variants as input. The SPL is constructed by extracting features from these variants. Features are identified and retrieved through feature location techniques (AL-Msie’deen et al. 2013 ; Rubin and Chechik 2013 ).

Reactive approaches follow an incremental process. They take as input an existing SPL version (SPL i ) and a set of new requirements about a new product. This process results in the creation of SPL i+1 , which can produce the new product.

2.2 Variability Management in SPL

In SPL engineering, variability mechanisms are fundamental for managing diversities across products. These mechanisms, as classified by Apel et al. ( 2013 ), include annotative mechanisms, transformative mechanisms (delta-oriented), and feature-oriented mechanisms. Annotative mechanisms involve marking or annotating code to denote variability points, while transformative mechanisms, such as delta-oriented programming, describe changes required to transform one product variant into another. Feature-oriented mechanisms organize variability around features and their interactions. These variability mechanisms can be applied across all stages of the software lifecycle.

A Feature Model is commonly used in Domain Engineering to present different combinations of features. A feature model is a formal representation and graphical notation that describes the variability and relationships among features in an SPL. Feature models typically consist of features (functionalities or characteristics), feature hierarchies (representing parent-child relationships between features), and constraints (rules governing the valid combinations of features) (Pohl et al. 2005 ). Due to the presence of numerous optional features, the configuration space in feature models may exponentially increase (reaching 2 n possible configurations, where n represents the number of optional features without further constraints) (Chen and Babar 2011 ). A specific product can be derived once a complete feature configuration is established.

Although proactive approaches emphasize systematic upfront planning, modeling variabilities with feature and configuration models, and high asset reusability, reactive methods can also use feature models to represent variabilities introduced by new requirements. Configuration files or mechanisms are often used in reactive approaches to specifying how variabilities are configured in reaction to new requirements (Ghanam et al. 2010 ). Furthermore, extractive approaches may employ feature models to represent and visualize variabilities discovered in existing products. Configuration scripts or files may be used to document and manage variabilities found in the codebase (Parra et al. 2012 ).

2.3 Testing approaches and levels

There exist diverse approaches to software testing, including (Luo 2001 ; Jorgensen 2013 ):

Manual testing : Testers create and execute test cases manually to evaluate the behavior of a software application or system without using automated testing tools or scripts.

Automated Testing : Specialized testing tools and scripts are used to automate the execution of test cases and the verification of software applications or systems.

Functional testing : Focuses on verifying software functions according to specified requirements. This approach includes different levels of testing, including:

Unit Testing is conducted at the lowest level, focusing on the fundamental unit of software, referred to interchangeably as “unit,” “module,” or “component.“

Integration Testing takes place when two or more tested units are integrated into a larger structure. This testing assesses the interactions between components and evaluates the quality of the overall structure when the properties cannot be determined solely from its individual components.

System Testing aims to validate the comprehensive quality of the entire system, covering end-to-end functionality. This type of testing typically aligns with the functional and requirement specifications of the system. Additionally, it assesses non-functional quality attributes like reliability, security, and maintainability.

Acceptance Testing occurs when the developers deliver the completed system to the customers or users. The primary goal of acceptance testing is to give confidence that the system functions correctly rather than to uncover errors.

Non-functional testing : Focuses on evaluating the attributes of a software system that are not directly related to its functional behavior. Instead, non-functional testing assesses the system’s performance, reliability, scalability, security, usability, and other qualities that impact the overall user experience and the system’s ability to meet non-functional requirements.

Regression testing : Focuses on verifying that recent changes or updates to a software application have not introduced new defects or negatively affected existing functionality.

Model-based testing : Test cases are derived from models representing the software’s expected behavior. Different models can be used to generate test cases systematically, including graphical representations, mathematical models, or formal notations.

SPL testing is an essential activity in SPLE to identify potential faults (Pohl and Metzger 2006 ). Exhaustive testing in SPL is usually infeasible due to a combinatorial explosion in the number of products. Following Tevanlinna et al. ( 2004 ), Reuys et al. ( 2005 ), Käköla and Dueñas ( 2006 ), there are specific differences between single-system testing and SPL testing:

Testing is a part of both phases: Domain Engineering and Application Engineering. Domain testing is focused on testing domain artifacts (e.g., requirements, features, and source code); however, as domain artifacts include variability, completely testing the domain artifacts in domain testing is impossible. Application testing aims to detect remaining faults in a derived product mainly caused by unexpected interactions.

Test assets created in Domain Engineering (e.g., test cases, test scenarios, test results, and test data) are reused in Application Engineering to test instantiated products. To this end, test assets should be created by considering variability, which we call variant-rich test assets.

3 Systematic literature review methodology

To carry out this SLR, we followed guidelines for performing SLRs in software engineering (Kitchenham and Charters 2007 ). The steps followed in conducting this SLR are developing a review protocol, conducting the review, analyzing the results, reporting the results, and discussing the findings. The review protocol used in this SLR is explained in the following subsections. The protocol includes the formulation of research questions to achieve the objective (Sect.  3.1 ), identification of sources to extract the research papers, the search criteria and principles for selecting the relevant studies (Sect.  3.2 ), specifying a set of criteria to assess the quality of each study remained for data extraction (Sect.  3.3 ), and developing the template used for extracting data (Sect.  3.4 ).

3.1 Research questions

As previously stated, this study aims to investigate how the existing approaches deal with testing in SPL. To formulate research questions, we examined topics addressed by previous research on SPL testing (Pérez et al. 2009 ; Engström and Runeson 2011 ; Da Mota Silveira Neto et al. 2011 ; do Carmo Machado et al. 2014 ). Some of the research questions were completely reused from previous research – i.e., RQ1, RQ2, RQ3, RQ6, and RQ7 – and some of them were formulated by analyzing specific aspects that have not been investigated in detail in previous research – i.e., RQ4 and RQ5.

We reuse RQs to contrast and compare the newer research contributions with the results of previous SLRs. Yet, we identified two unique, interesting aspects: Because testing every potential configuration of an SPL is often impractical, it becomes essential to employ specific approaches for identifying valid and invalid configurations. We have examined the techniques utilized or proposed in RQ4 to address this issue. Maintaining traceability between test assets and other SPL artifacts offers substantial advantages, including enhanced reusability, impact analysis, and change management. Consequently, we designed RQ5 to investigate the techniques employed for preserving traceability. Answering these questions led to a detailed investigation of the identified studies to specify practical and research issues regarding SPL testing; therefore, the results of this study can support both industrial and academic activities. The research questions are as follows:

RQ1. How is the research on SPL testing characterized? This question intends to discuss the bibliometrics of the primary studies and the evidence available to adopt the proposed approaches.

RQ2 . What levels of tests are usually executed throughout the SPL lifecycle (i.e., Domain Engineering and Application Engineering)? There are different levels of tests, and each level is associated with a specific development phase, including unit, integration, system, and acceptance tests (Ammann and Offutt 2008 ; Jaring et al. 2008 ). This question aims to specify different test levels usually executed throughout the SPL lifecycle.

RQ3 . How are test assets created by considering commonalities and variabilities? The large number of variation points and variants in an SPL increases the number of possible testing combinations. Creating test assets for all combinations of functionality is almost impossible in practice; therefore, test assets must be created by considering commonality and variability so that they can be reused as much as possible. Furthermore, an undetected error in common core assets of an SPL can be spread to all instances depending on those assets (Pohl and Metzger 2006 ); therefore, creating test assets by considering commonalities and variabilities and testing common aspects as early as possible is essential. Answering this question led to investigating how testing approaches handle commonality and variability throughout creating/executing test assets.

RQ4 . How do SPL approaches deal with configuration-aware software testing? Testing all functionality combinations in an SPL is impossible and unnecessary since some combinations are invalid based on the constraints defined between configuration parameters. This question is intended to specify ways/techniques to detect valid and invalid combinations of configuration parameters.

RQ5 . How is the traceability between test assets and other artifacts of SPL preserved throughout the SPL lifecycle? The reusability of test assets is essential to manage the complexity of SPL testing; preserving traceability between test assets and requirements/implementation can enhance the reusability of test assets. In this sense, this question is intended to identify specific ways/techniques to achieve traceability between test assets and other artifacts throughout the SPL lifecycle.

RQ6 . How are Non-Functional Requirements (NFRs) tested in SPL? NFRs such as security, reliability, and performance are very important for SPLs, and ignoring these requirements can lead to negative results (e.g., economic loss) (Nguyen 2009 ). Therefore, systematically testing NFRs by considering commonalities and variabilities is an important aspect of SPLE. This question is intended to investigate how tests of NFRs are performed in an SPL.

RQ7 . What mechanisms have been used for controlling cost/effort of SPL testing? As SPL testing is more expensive than single-system testing, identifying specific techniques to reduce effort can provide the reader with an initial list of techniques identified by analyzing the selected studies. The specified list can be enriched regarding new publications about SPL testing.

3.2 Identification of relevant literature

The process of gathering and selecting primary studies has been performed in three stages: in the first stage, we investigated previously published literature reviews on SPL testing (Pérez et al. 2009 ; Engström and Runeson 2011 ; Da Mota Silveira Neto et al. 2011 ; do Carmo Machado et al. 2014 ) to identify the initial set of papers that have been published up to 2013. In the second stage, we updated the list of papers by searching for new papers published between 2013 and 2022; in this stage, we performed forward and backward snowballing (Webster and Watson 2002 ) to identify missing relevant papers. In the third stage, we applied inclusion and exclusion criteria to each potential primary study identified through stages one and two. Each of the three stages is explained in detail in the following subsections. We must note that we chose studies that could address at least one of the RQs while selecting primary studies. For instance, certain studies focusing on SPL verification were included because they could provide insights relevant to questions such as RQ4. An Excel file was created to be shared among the authors to document the various steps of the SLR process. This file Footnote 1 contains all the details about how we gathered and selected primary studies and how we extracted data from the chosen studies.

3.2.1 Analysis of existing reviews

By searching for existing SLRs or Systematic Mapping Studies (SMSs) on SPL testing, we found four SLRs (Engström and Runeson 2011 ; Da Mota Silveira Neto et al. 2011 , Pérez et al. 2009 ; do Carmo Machado et al. 2014 ). Engström and Runeson ( 2011 ) conducted an SMS to identify useful approaches and needs for future research; in this study, 64 papers published up to 2008 were surveyed. Da Mota Silveira Neto et al. ( 2011 ) performed an SMS to investigate state-of-the-art testing practices in SPL testing; this study analyzed a set of 45 publications from 1993 to 2009. Pérez et al. ( 2009 ) conducted an SLR to identify experience reports and initiatives carried out in the SPL testing area; in this study, 23 primary studies published up to 2009 were analyzed. do Carmo Machado et al. ( 2014 ) conducted an SLR by analyzing 49 studies published up to 2013. As the four studies followed a systematic process to gather and select the primary studies, we are confident that they covered all the primary studies in the SPL testing field published up to 2013. Using the list of primary studies in the four SLR/SMS, a set of 181 potentially relevant papers was identified, shown as stage 1.1 in Fig.  1 . By reading the titles and abstracts of the publications, papers that addressed none of the research questions were excluded. Furthermore, duplicated papers were removed, i.e., those included in more than one literature review. At the end of this stage, 97 studies were finally selected, shown as stage 1.2 in Fig.  1 .

The process of gathering and selecting primary studies

3.2.2 Gathering recent publications

In the second stage of the search process, we updated the list of primary studies by analyzing papers published between 2013 and 2022 using the following databases: IEEE Xplore, Scopus, ACM DL, Springer, and Wiley online library. To answer the stated research questions, we identified the keywords that had to be used in the search process. Variants of the terms “ software product line ”, “ software product family ”, and “ software testing ” were applied to compose the search query, as follows:

(Software Product Line OR Software Product Lines OR Software Product Family OR Software Product Families) AND (Test OR Testing) .

To evaluate the search string, we first performed a limited manual search to see whether the results of that search were among the results obtained by running the search string. The search string was adapted based on the syntax requirements of each data source used. Table  13 in Appendix A shows the forms of search strings applied to different engines and the number of papers extracted from each data source.

We obtained a set of 2,608 papers by running the search string on the search engines, shown as stage 2.1 in Fig.  1 . We excluded 161 papers as duplicates since they were retrieved from multiple search engines. Furthermore, by reading the titles and abstracts of the remaining papers, a set of 2,125 papers was identified as irrelevant since they considered testing from a single-system development perspective, not an SPL point of view. At the end of this step, we had 322 papers, shown as stage 2.2 in Fig.  1 .

In the next step, we conducted both backward and forward snowballing by examining the reference lists of all the identified papers and exploring the papers that have cited these identified papers, respectively. Following this step, 70 additional papers (20 via backward snowballing and 50 via forward snowballing) were added to the previously identified set of papers, shown as stage 2.3 in Fig.  1 . At the end of stage 2, we had a set of 392 new publications, shown in Fig.  1 as stage 2.4.

3.2.3 Primary study selection strategy

By merging the results of the two previous stages, a set of 477 papers was composed, shown as stage 3.1 in Fig.  1 . Throughout the merging process, we identified 12 papers as duplicates because the year 2013 was considered in both the SLR conducted by do Carmo Machado et al. ( 2014 ) and in the automated search stage. We defined a set of inclusion and exclusion criteria to assess each potential primary study; the criteria are presented in Table  1 . These criteria were applied to the titles and abstracts of the identified papers. The first author performed this stage. However, to reduce the researcher bias, the results of this stage were validated by the second and third authors of this paper.

At this stage, we initially applied inclusion criteria to select papers meeting all of the specified criteria for inclusion. Following this, we applied exclusion criteria to exclude papers that met one or more of the specified exclusion criteria. We included only papers evaluated via at least one empirical method, including Case study, Survey, Experiment, and Observational study (Wohlin et al. 2003 ; Sjoberg et al. 2007 ; Zhang et al. 2018 ). At the end of this stage, a set of 161 papers were selected to be subject to full-text reading, depicted in Fig.  1 as stage 3.2. The analysis results of the papers, conducted based on the inclusion and exclusion criteria, are accessible within the replication package.

3.3 Quality assessment

Quality assessment of candidate studies is recommended to be performed to ensure that studies are impartially assessed for quality (Kitchenham et al. 2016 ). To this end, we used a set of quality criteria to examine the studies, shown in Table  14 in Appendix B. These criteria were reused from the criteria proposed by Dybå and Dingsøyr ( 2008 ) and cover four main aspects related to quality, including:

Reporting : Reporting of the study’s rationale, aims, and context.

Rigor : Has a thorough and appropriate approach been applied to key research methods in the study?

Credibility : Are the findings well-presented and meaningful?

Relevance : How useful are the findings to the software industry and the research community?

We used a weighting approach to examine the candidate studies in which two optional answers with their respective score were given for each question: “Yes” = 1, and “No” = 0. Then, we assigned a quality assessment score to each study by summing up the scores given to all the questions; the total quality score for each study ranged from 0 (very poor) to 11 (very good). The two authors assessed the papers, and any discrepancies were resolved by holding sessions with all the authors.

The first three criteria shown in Table  14 in Appendix B were used as the minimum quality threshold of the review to exclude non-empirical research papers. To this end, if question 1, or both of questions 2 and 3, received a “0” response, we did not continue the quality assessment process, and the paper was excluded. The results of the quality assessment for each paper are available in the replication package. Consequently, 43 papers were excluded, and 118 were selected as primary studies, shown in Fig.  1 as stage 3.3. The list of primary studies is presented in Table  15 in Appendix C.

The analysis of the studies based on quality assessment criteria is explained in more detail in Appendix E. In summary, concerning Reporting, most of the studies performed well. While the context description could be better in some studies, approximately 82% have clear research objectives, and all studies are based on research. On average, the studies performed reasonably well in terms of Rigor. Researchers have justified the research design in almost 62% of studies to accomplish the research’s goals. A base approach has been compared with the proposed approach in around 60% of studies, with the researchers attempting to prove that the selected controls reflect a defined population. Despite these promising findings, 32% of the studies fail in rigor. According to the credibility issue, around 95% of the studies discuss the results in relation to the research questions and highlight the study’s limitations. Most studies, however, need to establish relationships between the researcher and participants and the data collection that addresses the research problem. Regarding Relevance, about 97% of studies explicitly discuss SPL testing and how it contributes to existing knowledge, identifies new areas for research, and explains how the results can be used. Nevertheless, practitioner-based guidelines are present in about 15% of cases, indicating that more practical guidance is needed to strengthen industry adoption of SPL testing.

3.4 Data extraction and analysis

Data was extracted from each of the 118 primary studies during this stage. To this end, we used a predefined extraction form that enabled us to record the full details of the studies and be specific in answering research questions. The extraction form is shown in Table  2 . The first two authors conducted the process of reading and completing the extraction form; the data were extracted and stored in a spreadsheet after reading each paper and shared with all the authors. We followed the content structuring / theme analysis approach of Mayring ( 2014 ) to analyze the data. The types of extracted data from the extraction form already provided us with a list of themes and the corresponding extracted data for these themes. This step was deductive. In the next step, we inductively created categories in the themes to summarize them. All the authors held multiple sessions to discuss the intermediate results and resolve any potential discrepancies.

In the following sections, the data extracted from the primary studies is used to answer the research questions. An overview of the primary studies is first provided in Sect.  4.1 . Then, we answer each RQ via the extracted data.

4.1 Characteristics of the studies (RQ1)

This section discusses the bibliometrics of the primary studies, the evidence available to adopt the proposed approaches, and the results of the evaluations conducted based on the quality assessment criteria.

4.1.1 Bibliometrics

In this section, we analyze annual trends and distribution per venue type of the studies selected.

Annual trend:

The distribution of the primary studies according to publication year is shown in Fig.  2 . No publication prior to 2003 focuses on SPL testing. However, after 2003, there was at least one paper per year, except for 2004. As seen in Fig.  2 , the number of published papers in this field has generally increased over time (2003–2019). This indicates that the SPL Testing field has attracted the attention of many researchers in the last few years. Furthermore, it shows increasing attention to the use of empirical methods to assess the value of proposed approaches since we only included empirically evaluated studies in our review. As we excluded some of the papers based on the quality assessment criteria, there is no primary study published in 2004 that satisfies the minimum quality threshold of the review. Furthermore, the number of papers published in some years (e.g., 2013) was actually higher than the ones presented in Fig.  2 ; however, some of those papers were excluded throughout the assessment of quality criteria. It is worth mentioning that many studies might not be made available by search engines until the time the search was performed (August 2022), and thus, we did not consider these studies in this study. We have specified these studies in the replication package. The overall trend that the number of publications increases is quantified by all entries in DBLP for each year, as shown in Fig.  2 for comparison. As we see in this figure, the trend in SPL testing is well above in several years (2014, 2016, 2017, and 2019). However, this trend has been decreasing in recent years.

Distribution of primary studies by year

Distribution per venue:

Most of the primary studies were published in conferences; of 65 conference papers, 17 papers (∼ 26%) were published in SPLC Footnote 2 , which is the most representative conference for the SPL engineering area. This indicates that SPLC is an important venue for SPL research, and most primary studies in this field are presented in SPLC. Also, 31% of studies were published in journals, 7% in symposiums, and 5% in workshops.

4.1.2 Analyzing the evidence available to adopt the proposed approaches

As reported in the title or the text of the studies, case studies, experiments, and expert surveys are the specific methods that have been used for evaluating primary studies. Most of the primary studies were evaluated by conducting an experiment (∼ 58%). It is worth mentioning that five studies applied more than one evaluation method, including case study and expert survey (Bucaioni et al. 2022 ), case study and experiment (Akbari et al. 2017 ; Fragal et al. 2019 ), experiment and expert survey (Hervieu et al. 2016 ), and case study, experiment, and expert survey (Wang et al. 2017 ). Table  3 shows the primary studies that have used each type of evaluation method.

Although the studies reported that their proposed approaches were evaluated by using the mentioned empirical methods, we need to analyze the strength of the evidence available to adopt the proposed approaches. The results of this analysis can help researchers to find new topics for empirical studies, and practitioners to assess the maturity of a proposed approach. Kitchenham and Charters ( 2007 ) classified the study design into five levels, based on the evidence presented in medical research.

Alves et al. ( 2010 ) revised the classification to be applicable in their study; the revised classification is fully applicable in our review. The following hierarchy is used in our study (from weakest to strongest):

No evidence.

Evidence obtained from demonstration or working out toy examples.

Evidence obtained from expert opinions or observations.

Evidence obtained from academic studies, e.g., controlled lab experiments.

Evidence obtained from industrial studies, e.g., causal case studies.

Evidence obtained from industrial practice.

Based on the evidence evaluation scheme explained, the results of the evaluation on how much evidence is available to adopt the proposed approaches are presented in Table  16 in Appendix D. All the studies have been evaluated by one kind of evaluation method. Academic studies (Lev4) are the most used evaluation method (60%), where open-source repositories are usually utilized to assess the proposed approaches. Following is Demonstration (Lev2) (∼ 17%). Only a small number of studies have been evaluated by using industrial systems or real data sets (∼ 16%) (Industrial studies, Lev5), or by applying the proposed methods to industrial settings and by involving industrial professionals (∼ 13%) (Industrial practice, Lev6). This analysis shows an overall low level of evidence in the SPL testing field that is in line with the results of the SLR conducted by do Carmo Machado et al. ( 2014 ).

4.2 Test levels executed throughout the SPL lifecycle (RQ2)

We divided SPL testing according to the two common phases of SPLE: Domain Engineering and Application Engineering. Based on the analysis of the studies, there are two types of testing activities that are performed during Domain Engineering: (1) developing test assets so they can be instantiated in Application Engineering, (2) applying tests to assets produced during Domain Engineering to detect faults in common core assets as soon as possible. By analyzing studies that are focused on the second activity, we identified two levels of tests usually performed in Domain Engineering; distribution of studies based on the test levels is shown in Table  4 :

Unit testing : Out of 118 studies, three studies are only focused on this level of testing (Jaring et al. 2008 ; Kim et al. 2011 , 2012 ). Jaring et al. ( 2008 ) classified test levels based on the binding time of variabilities. Based on this study, unit tests are performed before variant binding; therefore, we included this study in this classification since Application Engineering is the phase in which variabilities are bounded to derive a specific product. Kim et al. ( 2011 ) and Kim et al. ( 2012 ) proposed specific methods in which analysis on the code level is performed to generate test suits for testing common parts of an SPL in Domain Engineering.

Integration testing : Execution of integration tests in Domain Engineering are examined in the studies by Reis et al. ( 2007 ), Neto et al. ( 2010 ) and Akbari et al. ( 2017 ). Reis et al. ( 2007 ) proposed a model-based, automated technique for integration testing in Domain Engineering. In the proposed technique, integration test case scenarios are generated to support the test of interactions between the components of an integrated sub-system; placeholders are also created for necessary variable parts and all components that are not part of the integrated sub-system. Neto et al. ( 2010 ) presented a regression testing approach for SPL architectures to maintain the correctness and reliability of the architecture after modifications; as the main purpose of the approach is to verify the integration among modules and components that compose the SPL architecture, we included this study in this classification. Akbari et al. ( 2017 ) proposed a method for prioritized selection and execution of integration test cases in both Domain Engineering and Application Engineering.

Specific testing activities that are conducted in Application Engineering are: Creating specific product test assets by selecting and instantiating domain test assets, designing additional product-specific tests, and executing tests (Da Mota Silveira Neto et al. 2011 ). It is worth mentioning that some of the studies are focused on reducing the number of products that need to be tested by using specific techniques like pairwise testing (e.g., Matnei et al. 2016 ). In addition, some studies are focused on product prioritization to enhance the efficiency of SPL testing (e.g., Parejo et al. 2016 ). Once a set of configurations/products are selected/prioritized for testing, their behavior needs to be tested using a specific mechanism, e.g. executable unit tests (Parejo et al. 2016 ). Studies that are focused only on the first step (selecting/prioritizing configurations) do not usually consider a specific level of test. The testing levels usually performed in Application Engineering, as shown in Table  4 , are as follows:

Unit testing : Some of the studies considered executing unit tests in Application Engineering (Bürdek et al. 2015 ; Li et al. 2018 ; Souto and d’Amorim 2018 ; Jung et al. 2019 , 2020 ; Lochau et al. 2014 ). Bürdek et al. ( 2015 ) proposed a white-box test-suit derivation mechanism for SPLs, specifically for unit testing, in which test specifications are extended with a presence condition. A presence condition constrains the set of configurations for which a specific test case is valid; this information is used for testing configurations in Application Engineering. Li et al. ( 2018 ) investigated test cases generated for one product that are reused for another product of the SPL by applying two categories of structure-based criteria, control-flow and data-flow. Souto and d’Amorim ( 2018 ), Jung et al. ( 2019 ) and Jung et al. ( 2020 ) identify unit test cases to be selected for regression testing.

Integration testing : As shown in Table  4 , this level of testing has been considered in a greater number of studies (27 studies). Some studies have not explicitly mentioned this level of testing; however, they mentioned that the untested parts of the framework are tested during Application Engineering (Scheidemann 2006 ; Al-Dallal and Sorenson 2008 ; Jaring et al. 2008 ). Some of the studies consider the selection of integration test cases during Application Engineering (e.g., Jung et al. 2019 ).

System /Acceptance testing : This level of testing has also been considered in a greater number of studies (28 studies), as shown in Table  4 . In most studies, test models designed throughout Domain Engineering are instantiated to derive specific system test cases (e.g., Olimpiew and Gomaa 2009 ). Arrieta et al. ( 2015 ) split the lifecycle of cyber-physical systems product lines into three phases: Domain Engineering, Application Engineering, and Simulation phases. Execution of system test cases are performed in the simulation phase; however, as we classified SPL lifecycle into Domain Engineering and Application Engineering, we included this study in this category.

4.3 Creating test assets by considering commonalities and variabilities (RQ3)

Creating test assets by considering commonality and variability to enhance their reusability and to reduce the probability of undetected errors in common core assets by testing them as early as possible is essential in SPL testing. Out of 118 papers, 25 primary studies (∼ 21%) provide contributions to handle variability in a range of different manners. We conducted an exploratory analysis to identify shared characteristics among the approaches and subsequently categorized them. We identified three categories of approaches, including model-, specification-, and requirements-based approaches. The distribution of studies based on these categories is shown in Table  5 .

Model-based approaches : In model-based approaches, a set of techniques is used to design and execute tests for SPLs by leveraging formal or semi-formal models of the SPL’s variability. In the examined studies, the subsequent methods are employed to incorporate variability into test models:

Adaptation of UML models or integrating them with the feature model to produce test models including variability : In studies (Reuys et al. 2005 , 2006 ; Reis et al. 2007 ; Olimpiew and Gomaa 2009 ), activity diagrams are extended using specific mechanisms (e.g., stereotyping specific elements) to contain variabilities and then used as test models to create domain test case scenarios. Ebert et al. ( 2019 ) developed a common platform in Domain Engineering that contains all elements required for producing products. This study uses the SMArDT methodology (Drave et al. 2019 ) to elaborate each functionality defined in the platform via an extended version of activity diagram; generic test cases are then created for each functionality based on the SMArDT methodology. Reis et al. ( 2006 ) propose the ScenTED-PT technique in which the requirements and the architecture of the system are specified by UML models supplemented with performance requirements; then, they create a test model from which performance test case scenarios are derived.

Lochau et al. ( 2012a ) and Lackner et al. ( 2014 ) proposed to use the statechart modeling approach as a basis for capturing commonalities and variabilities of product implementations in an SPL; a 150% statechart model and the feature model is integrated to produce a reusable test model. The 150% statechart model is a model that contains the behavioral specification fragments of every feature without considering constraints between features, and the 100% statechart model is a specific instantiation of the 150% model by considering the dependencies and constraints (Lochau et al. 2012a ).

Using/defining different modeling notations to capture variabilities and using them to produce test assets : In this category of model-based approaches, specific modeling notations have been used or defined to create variant-rich test models. Tuglular et al. ( 2019 ) introduced Featured Event Sequence Graphs (FESGs) to explicitly capture behavioral variability in SPLs. Gebizli and Sözer ( 2016 ) used hierarchical Markov chains to model system usage; as this model captures all possible usage scenarios for a family of systems, it is considered as a reference test model. Bucaioni et al. ( 2022 ) define specific metamodels and languages to capture test variabilities, including SPL metamodel (SPLmm), Products metamodel (Pmm), Weaving metamodel (Wmm) to link features and signals in Pmm to those in SPLmm, Test case DSL (TcDSL), and Test Script generation Transformation (TsT). Fragal et al. ( 2019 ) use Featured Finite State Machines (FFSMs) to represent the abstract behavior of an SPL; in this study, the HSI method (Luo et al. 1995 ) has been extended to generate a single configurable test suite for an SPL. Luthmann et al. ( 2019a ) extended the concept of Timed Automata (TA) by feature constraints and configurable parameters to facilitate efficient verification of real-time properties for SPLs. Lochau et al. 2012b ), Lachmann et al. ( 2016 ), and Lity et al. ( 2019 ) apply the principles of delta modeling (Schaefer et al. 2010 ) to state machine test models to explicitly capture behavioral commonality and variability between product variants and then their test assets. In delta-oriented testing techniques, a product is considered as a base product and delta modules specify changes that should be applied to the base product to produce new ones (Schaefer et al. 2010 ). Beohar and Mousavi ( 2016 ) introduce the concept of Input-Output Featured Transition Systems (IOFTSs); IOFTSs are labeled transition systems with logical constraints on the presence or absence of features and are used as test models. In the work by Lochau et al. ( 2014 ), they introduced delta-oriented architecture test modeling as a means to systematically reuse common component and integration test elements across various system variants. They employed delta-oriented test artifact reuse and regression test planning to facilitate the systematic evolution of variable test elements among incrementally tested versions and/or variants of a software system.

Specification-based approaches : In these approaches, specific links are defined between different configurations of an SPL and, therefore, between test cases designed for both shared and variable components of the products. Mishra ( 2006 ) uses the process algebraic specification language CSP-CASL (Roggenbach 2006 ) to formally specify the system; then, enhancement relationships are established between the specifications of products. In this way, test cases generated for the common parts are reused between products, and new test cases are generated for the differences in the specification. Uzuncaova et al. ( 2010 ) describe properties of features as first-order logic formulas in Alloy (Jackson 2012 ); by considering a product as a base, test cases are generated for the base product using Alloy Analyzer. For each new product, the test cases from previous products are reused/refined based on the differences in the specifications.

Requirement-based approaches : In these approaches, variability is considered as early as possible so that it can be used to design test cases. In several primary studies, use case modeling is the approach used for representing requirements (Nebut et al. 2006 ; Araújo et al. 2017 ; Hajri et al. 2020 ). Nebut et al. ( 2006 ) enhance use cases with parameters and contracts used for presenting variability at the level of requirements; test-related artifacts (e.g., test objectives, test scenarios, and behavioral test patterns) are produced based on the enhanced use cases. Araújo et al. ( 2017 ) express use case specifications in a controlled natural language by considering variabilities; the specifications are then used for generating test procedures and their input and output. Hajri et al. ( 2020 ) propose to use the Product line Use case modeling Method (PUM) that supports variability modeling in use case diagrams; by using the requirement traceability mechanism, test cases for a new product are generated by reusing/adapting existing test cases or by defining new test cases.

Kang et al. ( 2015 ) propose a method called Systematic Software Product Line Test - Data (SSPLT-D) in which a set of platform test requirements are first defined throughout Domain Engineering and then platform test scenarios, platform test cases, and platform test data are created based on test requirements. Nebut et al. ( 2003 ) propose to derive a set of behavioral test patterns from the requirement model and then use them to produce product-specific test cases.

4.4 Dealing with configuration-aware software testing (RQ4)

Dealing with configuration-aware software testing, i.e., detecting valid and invalid combinations of configuration parameters, is paramount in SPL approaches because testing all combinations of SPL functionalities would be impossible and unnecessary. In our investigation, 41 out of 118 papers (∼ 35%) have addressed this. These papers have employed three methods to distinguish between valid and invalid configurations; distribution of studies based on these methods is shown in Table  6 :

Using/proposing specific approaches/algorithms/tools to produce valid configurations : Some studies utilize constraint programming, which is used for solving and modeling constraint satisfaction problems, to generate configurations that satisfy all cross-tree constraints imposed by the feature model (Hervieu et al. 2011 ; Marijan et al. 2013 ). In the same way, Kim et al. ( 2013 ) and Akbari et al. ( 2017 ) propose a constraint handling approach to produce valid configurations; as an example, an algorithm called SPLat is proposed in study (Kim et al. 2013 ) that dynamically prunes irrelevant configurations by handling constraints.

Using formal methods to check cross-tree constraints defined in feature models to check the relations between features is another way to find and produce valid configurations (Lackner et al. 2014 ; Lopez-Herrejon et al. 2014 ; Beohar and Mousavi 2016 ; Parejo et al. 2016 ; Ferrer et al. 2017 , 2021 ; Akimoto et al. 2019 ; Arrieta et al. 2019 ; Jakubovski Filho et al. 2019 ; Luthmann et al. 2019b ; Ibias et al. 2022 ). For example, Lackner et al. ( 2014 ) transform a feature model into propositional formulas so that any variable assignment that satisfies the formula is a valid configuration for the product line.

Several studies suggest the utilization of sampling algorithms and techniques to generate valid configurations (Oster et al. 2010 ; Lochau et al. 2012a ; Patel et al. 2013 ; Yu et al. 2014 ; Al-Hajjaji et al. 2016 , 2019 ; Lee and Hwang 2019 ). Combinatorial Interaction Testing (CIT) is among the commonly used sampling algorithms to exclude invalid interactions between features; in CIT, design-time decisions for variability are considered to exclude invalid interactions between features. For example, Oster et al. ( 2010 ) and Lochau et al. ( 2012a ) propose a pairwise algorithm in which dependencies and constraints between each pair of features are considered to generate all possible products that cover all valid pairs of features and their potential interactions. In a study conducted by Saini et al. ( 2022 ), they introduced a distance-based method for recognizing invalid configurations. This approach involves an initial phase where specific CIT algorithms are employed to generate real configurations. Following that, desired configurations are created, considering the availability of features in the configurations. The approach distinguishes valid from invalid configurations by applying a comparison technique to assess the differences between the actual and desired configurations.

Additionally, several studies proposed tool support for their specific approaches. They used SAT solvers to generate configurations to satisfy the feature model constraints which, in turn, reduces the configuration space to be tested (Henard et al. 2013 , 2014a , b ; Galindo et al. 2016 ; Hervieu et al. 2016 ; Souto and d’Amorim 2016; Fragal et al. 2019 ; Luthmann et al. 2019a ; Krieter et al. 2020 ; Xiang et al. 2022 ). Using or implementing a tool or toolkit to produce valid configurations has been proposed by Ensan et al. ( 2012 ), Al-Hajjaji et al. ( 2016 ), Arrieta et al. ( 2016 ), Al-Hajjaji et al. ( 2019 ) and Arrieta et al. ( 2019 ). For example, FeatureIDE has been used in studies by Al-Hajjaji et al. ( 2016 ), Arrieta et al. ( 2016 ), Al-Hajjaji et al. ( 2019 ), and Arrieta et al. ( 2019 ); this tool can generate valid configurations manually and automatically.

Runtime analysis : An alternative category of methods employs runtime analysis to differentiate intended from unintended interactions. In these methodologies, rather than relying on pre-established specifications to detect interactions, they examine runtime data to distinguish valid and invalid interactions (Reuys et al. 2006 ; Lochau et al. 2014 ; Rocha et al. 2020 ; Vidal Silva et al. 2020 ). As an example, in a study by Rocha et al. ( 2020 ), they introduced an iterative technique called VarXplorer to inspect interactions as they emerge. When provided with a test case consisting of system inputs, VarXplorer generates a Feature Interaction Graph (FIG), which is a concise representation of all pairwise interactions among features. This FIG offers a visual depiction of the features that interact, the contextual data, and the relationships between features, including cases where one feature suppresses another. By employing an iterative approach to interaction detection, developers and testers can thoroughly analyze the FIG derived from all the test cases within a test suite.

It is worth mentioning that some studies only stated that the feature model is manually analyzed to consider feature dependencies and feature grouping constraints (Olimpiew and Gomaa 2009 ; Cabral et al. 2010 ).

4.5 Preserving traceability between test assets and other artifacts (RQ5)

One of the essential factors in SPL testing is the preservation of the traceability between test assets and other artifacts throughout the SPL lifecycle. This is due to enhancing the reusability of test assets for managing the SPL testing complexity. However, in this regard, a few papers take preserving traceability into account, only 14 out of 118 (∼ 12%). We categorized these papers according to the type of the artifacts linked to test assets; distribution of studies based on this classification is shown in Table  7 :

Preserving traceability between requirements and test assets : In the majority of the studies, traceability is established between requirements, often represented using UML models (primarily use cases), and various test assets. These papers have utilized various methods, encompassing the gradually refinement of UML models into test models, direct mapping of requirements to test assets, annotation-based traceability, and the application of specific tools for automated tracing.

Reuys et al. ( 2005 ), Nebut et al. ( 2006 ), Reis et al. ( 2007 ) and Olimpiew and Gomaa ( 2009 ) use UML models to preserve the traceability between requirements and test case scenarios. In the same way, Reuys et al. ( 2006 ) enabled the traceability between different artifacts (use cases, use case scenarios, architecture scenarios, and test case scenarios) by refining use case scenarios into test case scenarios.

The manual definition of links between use cases and system test cases was mentioned by Hajri et al. ( 2020 ). Lackner et al. ( 2014 ), Gebizli and Sözer ( 2016 ) and Wang et al. ( 2017 ) created mapping relationships between variabilities modeled via the feature model and the test model to preserve traceability between requirements and test assets. Bucaioni et al. ( 2022 ) employed a metamodel to create a link between the product models and the SPL model. In this approach, the shared functionalities of the SPL are represented through a class diagram, and test cases are generated explicitly for these shared functionalities.

Adding annotations to test assets to specify their relationship with other artifacts is the approach proposed by Marijan et al. ( 2017 ); in this approach, test cases were manually annotated using tags and related to one or more test requirements; this traceability information is then used to assess the quality of test cases with respect to the requirements coverage.

In some studies, specific tools are used for automated tracing (Reis et al. 2006 ; Lochau et al. 2012a ). Reis et al. ( 2006 ) use a tool named Mercury TestDirector to preserve the traceability between requirements specification, domain performance test case scenarios, and application performance test case scenarios. Lochau et al. ( 2012a ) employed Rhapsody ATG to enable traceability between requirement models and test artifacts in an automated manner.

Preserving traceability between configurations and test assets : The solution proposed by Mishra ( 2006 ) is the definition of enhancement relationships between specifications of systems (different configurations of the SPL) and, therefore, their test cases.

It is also worth mentioning that some studies have emphasized the importance of preserving traceability between test assets and other artifacts, but they provide no mechanism in this regard (Kang et al. 2015 ; Aduni Sulaiman et al. 2019 ).

4.6 Testing non-functional requirements in SPL (RQ6)

In addition to functional requirements, there are non-functional requirements which should be tested in SPL, but only 3 out of 118 studies consider them. Various categories of NFRs have been addressed in these studies, including load testing and performance profiling (Reis et al. 2006 ), NFRs at the hardware-in-the-loop level (Arrieta et al. 2016 ), and real-time properties (Luthmann et al. 2019a ).

Reis et al. ( 2006 ) propose a technique which concentrates on load testing and performance profiling. They employ the Object Management Group’s UML Profile (Fomel 2002 ) to model performance aspects. Testing NFRs as a critical aspect of cyber-physical systems is investigated at the hardware-in-the-loop level by Arrieta et al. ( 2016 ); these requirements (e.g., the usage of memory and CPU) are modeled via the feature model and their coverage is considered by using selected test cases and the simulation process. Luthmann et al. ( 2019a ) present configurable parametric timed automata to extend the expressiveness of featured timed automata to enable efficient family-based verification of real-time properties (e.g., synchronization and execution time behaviors); the proposed modeling formalism aims to represent the behavioral variability of time-critical product lines and consider the minimum/maximum delay coverage.

4.7 Controlling cost/effort of SPL testing (RQ7)

As the cost/effort of SPL testing remains a significant concern within SPLE, numerous studies have proposed various techniques to address this issue. However, the lack of a standardized classification for these techniques has made it challenging to analyze them effectively. One notable exception is the extensive research conducted on product sampling techniques, which has been categorized into specific sub-techniques, including automatic selection, semi-automatic selection, and coverage (Varshosaz et al. 2018 ). In our analysis, we utilized these established categories to organize the diverse range of techniques proposed in the literature.

While reviewing the papers, we identified other approaches that offer potential solutions to managing the cost and effort associated with SPL testing. These approaches were categorized based on their primary contributions and grouped into distinct categories. Some of the identified approaches focus on the reuse of test assets, either from a core asset base or from previously tested products. Others provide varying degrees of automation, ranging from the implementation or utilization of specialized tools to the automation of specific testing processes, such as specification-based approaches.

Additionally, a subset of studies explored strategies for prioritizing the execution order of SPL configurations or products and the associated test cases. Another category of research aimed to minimize the size of the test suite required for testing a particular product, thereby reducing overall testing effort.

It is important to note that these techniques can often be combined. For example, test prioritization and minimization techniques can be used with sampling techniques to further optimize the cost and effort associated with SPL testing. Furthermore, the list of techniques can be enriched concerning new publications regarding SPL testing. In the rest of this section, the details of these five techniques are provided:

Reusing test assets : Based on the analysis of studies, test assets (e.g., test cases and test results) are reused in two ways, including:

Reusing test assets from a core asset base : In some studies, domain test scenarios containing variabilities are created in Domain Engineering; some of these scenarios are reused, and some of them are adapted based on the application requirements (Nebut et al. 2003 ; Reuys et al. 2005 , 2006 ; Reis et al. 2006 ). Some other studies are focused on reusing test cases by selecting them from a repository based on the application requirements (Arrieta et al. 2016 ; Wang et al. 2017 ; Lima et al. 2020 ) or by binding variabilities defined in abstract test cases based on specific criteria (e.g., coverage criteria) (Al-Dallal and Sorenson 2008 ; Olimpiew and Gomaa 2009 ; Lackner et al. 2014 ; Bürdek et al. 2015 ; Kang et al. 2015 ; Ebert et al. 2019 ; Fragal et al. 2019 ; Luthmann et al. 2019a ).

Reusing test assets between products : In some studies, test assets are reused between products by analyzing differences between the current product and previously tested products (Mishra 2006 ; Uzuncaova et al. 2010 ; Neto et al. 2010 ; Lochau et al. 2012b , 2014 ; Xu et al. 2013 ; Lachmann et al. 2015 , 2016 ; Beohar and Mousavi 2016 ; Fragal et al. 2017 ; Li et al. 2018 ; Ebert et al. 2019 ; Lity et al. 2019 ; Luthmann et al. 2019a ; Tuglular et al. 2019 ; Hajri et al. 2020 ). The technique usually used in these studies is the delta-oriented testing technique, based on regression testing principles and delta modeling concepts. By considering delta modules, test cases and test results from previously tested products can be reused and adapted for the new product.

Providing a specific level of automation : We found two ways by which the studies provide a particular level of automation:

Implementing/using a specific tool(s) : In 49 studies, authors claimed that their proposed approach is automatically performed using specific tools. However, the majority of these studies fail to provide any details regarding the specific tools employed for this purpose (e.g., Reis et al. 2006 ; Olimpiew and Gomaa 2009 ; Calvagna et al. 2013 ; Li et al. 2018 ; Safdar et al. 2021 ). Table  8 shows that only 19 of these studies have provided online access to their tools. It is worth noting that most of these tools are in the form of research prototypes. Instead of developing a novel tool tailored to their proposed approach, some studies utilize a set of pre-existing tools at various stages of their approach. For instance, in the case of Parejo et al. ( 2016 ), the Combinatorial tool and Feature Model Testing System (FMTS), as introduced by Ferreira et al. ( 2013 ), were employed to derive pairs and calculate solution fitness, respectively.

Using specific techniques that help automate the testing process. Specification-based testing was used in some studies (e.g., Mishra 2006 ) as an appropriate step in automating the testing process because of its precise nature in describing the desired properties of the system under test by using a formal language. Model-based testing is another approach that helps automate the testing process. For example, Bucaioni et al. ( 2022 ) introduced a model-based approach in which test scripts are generated from shared SPL features by model transformation.

Handling the selection of products to test : Testing all possible combinations of features is almost impossible in terms of resources and execution time (Cohen et al. 2006 ). Specific approaches have been proposed to determine a minimal set of configurations so that the correctness of the entire family can be inferred by successful verification of this set. Through our examination of the studies, we have identified diverse techniques for choosing a subset of products. These techniques have been categorized according to the provided categories for product sampling in study (Varshosaz et al. 2018 ). Distribution of studies based on these techniques are shown in Table  9 :

Automatic selection : There are two general types of automatic selection techniques, including Greedy and Meta-heuristic search:

Greedy : Greedy algorithms (Vazirani 2001 ) are focused on finding an optimal solution by an iterative approach. In the context of SPLs, the optimal solution is the configuration most close to the optimum. Specific measures are used to determine a configuration as an optimum solution in each iteration (e.g., requirements/feature coverage).

Meta-heuristic search : In this category, the problem of identifying a subset of products is considered as an optimization problem. Meta-heuristic algorithms are designed to target this problem by employing computational search within the configuration space to find an optimal subset of products (Varshosaz et al. 2018 ). Some studies have applied Evolutionary Algorithm, Random Search, and Genetic Algorithm by using an aggregation function of different objectives such as cost, number of products, number of revealed faults, pairwise coverage, and mutation score (e.g., Ensan et al. 2012 ). Some other studies propose to use multi-objective algorithms (e.g., Matnei et al. 2016 ). Hyper-heuristics are another category of approaches that have been explored in some studies to solve the problem of product sampling (e.g., Strickler et al. 2016 ). A hyper-heuristic is a methodology that can help automate configuration of heuristic algorithms and determine low-level heuristics (Jakubovski Filho et al. 2018 ). To consider user preferences throughout the selection of products as well as to make use of benefits of hyper-heuristic approaches, a preference-based hyper-heuristic approach has been proposed by Jakubovski Filho et al. ( 2018 ); this approach is an example of algorithms proposed in the field called Preference and Search-Based Software Engineering (PSBSE) (Ferreira et al. 2017b ).

Semi-automatic selection : In semi-automated selection, various factors are considered, including the desired number of generated products, the allocated sampling time, and the level of coverage, such as coverage of feature interactions. Moreover, the complete sample set or an initial set produced by other sampling techniques may serve as a starting point for the sampling process (Varshosaz et al. 2018 ). As an example, Reuling et al. ( 2015 ) propose a framework for fault-based (re-)generation of configuration samples based on feature-diagram mutation. The underlying rationale for this approach is rooted in the recognition that subsets of products generated by CIT approaches can often contain numerous redundant or less significant feature combinations. Furthermore, these approaches may overlook crucial or error-prone combinations beyond t-wise, primarily due to their black-box nature, which typically lacks consideration of domain-specific knowledge, including the fault history associated with feature combinations. The authors argue that the integration of their proposed approach with pairwise CIT sampling can potentially enhance the efficiency and effectiveness of SPL testing.

Coverage : Coverage criteria are frequently employed to ensure the quality of product sampling. One commonly utilized criterion is the coverage of feature interactions (Varshosaz et al. 2018 ). CIT techniques are focused on the interactions between different features or configuration options, as these interactions often lead to defects in software systems. These techniques are classified as greedy by Cohen et al. ( 2007 ) since they are focused on selecting a subset of configurations where each configuration covers as many uncovered combinations as possible. However, it is categorized separately in some other studies (e.g., Cmyrev and Reissing 2014 ). We also prefer to separate this category of techniques from greedy algorithms since they are specially focused on covering feature interactions. The studies that provide details of either a process or an algorithm for CIT are shown in Table  9 .

The most popular kind of CIT is pairwise testing (2-wise), a specialized notion of t-wise coverage; in t-wise testing, configurations are selected in a way that guarantees that all combinations of t features are tested. Kuhn et al. ( 2004 ) showed that 80% of bugs can be revealed by investigating interaction between two variables. Furthermore, for solving problems of large complexity, pairwise has proven to be most effective since finding inconsistencies in a model including only two features might be easier than investigating all combinations of features at once (do Carmo Machado et al. 2014 ). However, Steffens et al. ( 2012 ) revealed that the interaction of three or more features usually occurs in the SPL testing field; therefore, considering the combination of high-strength can have an important role in revealing faults. To this end, some studies claimed that their proposed approach for t-wise coverage can work with any value of t (e.g., Krieter et al. 2020 ). However, high-strength (t > 3) feature interaction can lead to a large number of valid configurations and therefore complicate the problem of t-wise coverage (Qian et al. 2018 ). Therefore, selecting a specific value for t is usually a trade-off between cost and efficiency to reveal faults.

Prioritizing configurations/test cases : Test case prioritization is focused on defining the execution order of test cases that attempts to increase their effectiveness at meeting some performance goals (Li et al. 2007 ; Catal and Mishra 2012 ). By investigating studies, we found two categories of studies in this regard:

Several studies propose approaches for prioritizing SPL configurations/products to be tested; these approaches are usually used as a complement for product selection/sampling techniques. In some of these studies, one or more objectives are defined for configuration prioritization (e.g., high failure rate and high overall requirement coverage) (Scheidemann 2006 ; Sánchez et al. 2014 ; Wang et al. 2014 ; Galindo et al. 2016 ; Parejo et al. 2016 ; Akimoto et al. 2019 ; Hierons et al. 2020 ; Pett et al. 2020 ; Ferrer et al. 2021 ); results of the evaluations conducted by Parejo et al. ( 2016 ) indicate that multi-objective prioritization typically leads to faster fault detection than mono-objective prioritization. In another category of studies, similarity between configurations with respect to feature selections is considered as a criterion for product prioritization (similarity-based prioritization) (Arrieta et al. 2015 ; Al-Hajjaji et al. 2017a , 2019 ). In these approaches, configurations are prioritized based on the dissimilarity between them so that the configuration that has the lowest value of similarity compared to previously selected configurations in terms of feature selections is chosen. Al-Hajjaji et al. ( 2017b ) propose a delta-oriented product prioritization method as similarity-based prioritization techniques do not consider all actual differences between products; in this approach, instead of comparing products to select features, delta-modeling artifacts (Clarke et al. 2010 ) are used to prioritize products.

Some studies are focused on prioritizing test cases for products. Lima et al. ( 2020 ) propose a learning-based approach is proposed to prioritize test cases in the Continuous Integration (CI) cycles of Highly Configurable Systems (HCI). Arrieta et al. ( 2015 ), Marijan et al. ( 2017 ), Markiegi et al. ( 2017 ), Arrieta et al. ( 2019 ) and Hajri et al. ( 2020 ) use specific criteria to prioritize the test cases (e.g., Fault detection capability, Test execution time, or Test case appearance frequency). In another category of studies, similarity-based approaches are proposed to prioritize test cases (e.g., Devroey et al. 2017 ; Lachmann et al. 2015 ; Lachmann et al. 2016 ). As an example, Devroey et al. ( 2017 ) propose an algorithm to generate and sort dissimilar tests to achieve good fault finding; to this end, a distance function is calculated based on the actions executed by the test case. Furthermore, to provide a good coverage of a large number of products, prioritizing test cases is also performed based on the products that may execute a test case.

Minimizing test suite : This technique is focused on minimizing the test suite size for testing a product, while preserving fault detection capability and testing coverage of the original test suite. Al-Dallal and Sorenson ( 2008 ), Stricker et al. ( 2010 ), Kim et al. ( 2012 ) and Beohar and Mousavi ( 2016 ) discuss approaches in which test cases already covered during Domain Engineering or test cases related to common parts that have already been executed in previous products are ignored. Other studies propose specific approaches to reduce redundant test executions for SPL regression testing by pruning tests that are not impacted by changes (Lachmann et al. 2016 ; Jung et al. 2019 , 2020 , 2022 ; Souto and d’Amorim 2018 ).

There are studies focused on improving test generation process to produce minimal set of test cases while achieving specific objectives (e.g., coverage and cost/time) (Patel et al. 2013 ; Wang et al. 2015 ; Gebizli and Sözer 2016 ; Akbari et al. 2017 ; Marijan et al. 2017 ; Aduni Sulaiman et al. 2019 ; Markiegi et al. 2019 ; Rocha et al. 2020 ). As an example, Akbari et al. ( 2017 ) propose a method in which features in feature model are prioritized based on the domain engineer’s decisions and the constraints that exist between features; integration test cases are then produced by considering specified priorities. Furthermore, there are approaches that are not directly focused on test suit minimization; however, they help reduce redundant execution of tests for unnecessary configurations (Kim et al. 2013 ; Souto and d’Amorim 2018 ). These approaches are focused on removing the valid configurations that are unnecessary for the execution of each test.

The distribution of studies based on the identified techniques is presented in Table  10 . As observed, the majority of the studies (∼ 62%) are focused on proposing a specific level of automation. However, many of these studies do not offer details regarding the specific tools utilized for this purpose. The second most researched category of approaches pertains to handling the selection of products to test (∼ 39%). Following this are techniques involving reusing test assets (∼ 25%), prioritizing configurations/test cases (∼ 18%), and minimizing test suite size (∼ 15%).

5 Threats to validity

In this section, we discuss the main threats associated with the validation of this study, classified according to the categorization proposed by Ampatzoglou et al. ( 2019 ). These particular threats are categorized into three categories: study selection validity, data validity, and research validity.

5.1 Study selection validity

One of the main threats to any secondary study is its inability to guarantee the inclusion of all relevant articles in the field. To mitigate this threat, a meeting involving all researchers was conducted to discuss and refine the search scope and keywords. Then, we evaluated the validity of the search string by conducting a limited manual search to see whether the results of that manual search show up in the results obtained by running the search string.

To ensure the comprehensive identification of all relevant studies in our search process, we rigorously followed the guidelines provided by Kitchenham and Charters ( 2007 ). We conducted a bibliographic search of published literature reviews in the SPL testing field. We updated the list of studies by applying a search string to multiple digital libraries and performed the backward and forward snowballing process. Therefore, we are confident that we have provided good coverage of studies in the SPL testing field.

During the primary study selection process, to minimize potential bias in applying inclusion/exclusion criteria, these criteria were clearly defined and regularly updated in our protocol. The first author applied inclusion and exclusion criteria. However, to reduce the researcher bias, the results of this stage were validated by the second and third authors of this paper.

Regarding quality assessment, we used a set of quality criteria to examine the studies. These criteria were reused from those proposed by Dybå and Dingsøyr ( 2008 ). Two researchers participated in the application of quality assessment criteria. We also conducted regular meetings to address and resolve any conflicts that arose during the process effectively.

5.2 Data validity

One of the main threats regarding data validity is data extraction bias. Subjective bias during the data extraction process has the potential to lead to an inconsistent interpretation of the extracted data by researchers. To mitigate this risk, two researchers collaborate during the data extraction phase, conducting resolution sessions to address any emerging ambiguities. Nevertheless, due to certain studies needing more explicit details on specific aspects of SPL testing, such as test levels, we had to make subjective interpretations based on information scattered throughout these studies.

Subjective bias may also lead to the misclassification of data in response to RQ3–RQ7. Since no predefined categories were available, we adopted an exploratory approach, scrutinizing the extracted data and identifying pertinent categories. To mitigate this potential issue, we introduced a structured data extraction form, conducted quality assessments on the chosen studies, and maintained ongoing discussions to ensure consistency in the data extraction process and category definitions. However, it is essential to acknowledge the potential influence of researcher bias on data extraction and presentation within this study.

5.3 Research validity

Research validity encompasses threats identified at all stages of our SLR.

We extensively searched secondary studies, as detailed in Sect. 3.2. This approach enabled us to identify research gaps, consider the scope and definition of RQs, and gain insights into the current state-of-the-art within the domain of SPL testing.

In our exploration of potential threats to the repeatability of this SLR, we acknowledge the complexity inherent in replicating research. Specifically, we highlight the concern that other researchers may not repeat the SLR with precisely the same results. To mitigate this threat, we provided the details of the SLR methodology so that other researchers can replicate the study; furthermore, we have made all the data collected during the SLR process available online. However, as subjectivity in the studies analysis is one major issue in conducting a literature review, we cannot guarantee that researchers can achieve exactly the same results.

One serious threat to the validity of the SLR is the inability to generalize the study’s results to other scenarios and application domains. We included only the studies empirically evaluated in our analysis to handle this threat. However, as most evaluations do not refer to real-world practice, the results and classifications presented in this study may not fully apply to practical settings. Moreover, our SLR intentionally focused exclusively on SPLs. This deliberate choice was made to answer specific questions tailored for SPL testing. While this focus enhances the depth of our insights into SPL testing practices, it inevitably limits the applicability of our findings to the broader context of configurable systems. The decision not to include configurable systems was strategic, considering the extensive body of literature on configurable system testing, which would have required substantial additional time and effort for comprehensive analysis.

6 Discussion

In this study, we presented a systematic review of testing approaches proposed in the SPLE field. We have investigated seven RQs:

RQ1: How is the research on SPL testing characterized?

The analysis indicates that the SPL testing field has attracted significant attention from researchers in recent years, with an increase in empirically evaluated studies. Although the overall number of publications has grown, recent years have seen a decline. Most primary studies are published in conferences, with case studies, experiments, and expert surveys being the common evaluation methods. However, the strength of evidence supporting the proposed approaches varies, with academic studies (60%) being the most common, followed by demonstrations (17%). Only a small number of studies involve industrial systems or real data sets (16%) or industrial practice (13%), indicating an overall low level of evidence in the field.

RQ2 . What levels of tests are usually executed throughout the SPL lifecycle (i.e., Domain Engineering and Application Engineering)?

In Domain Engineering, testing activities include developing test assets for later use and testing assets to detect faults early. In Application Engineering, activities involve creating specific product test assets, designing additional product-specific tests, and executing tests. Some studies focus on reducing the number of products tested or prioritizing products to enhance testing efficiency. The distribution of studies based on test levels shows that in Application Engineering, integration testing and system/acceptance testing are the most commonly reported levels. In contrast, unit testing is less frequently reported in both phases. This indicates a strong focus on higher levels of testing in the SPL testing field, particularly in the Application Engineering phase.

RQ3 . How are test assets created by considering commonalities and variabilities?

Creating test assets to address commonality and variability in SPL testing is crucial for enhancing reusability and minimizing faults in core assets. Our analysis categorized these approaches into three groups: model-based, specification-based, and requirement-based.

Model-based approaches utilize formal or semi-formal models of SPL variability to design and execute tests. Specification-based approaches define specific links between different SPL configurations and test cases. Requirement-based approaches prioritize considering variability early in test case design. The distribution of studies across these categories indicates that model-based techniques are the most commonly used in the examined studies.

RQ4 . How do SPL approaches deal with configuration-aware software testing?

Dealing with configuration-aware software testing, particularly in distinguishing valid and invalid combinations of configuration parameters, is crucial in SPL approaches. Testing all possible combinations of SPL functionalities is not only impractical but also unnecessary. The studies have employed three main methods to distinguish between valid and invalid configurations: Using/proposing specific approaches, algorithms, or tools, runtime analysis, and manual analysis. The distribution of studies across these methods indicates that the majority of the studies have either proposed specific methods or algorithms or have utilized already available tools.

RQ5 . How is the traceability between test assets and other artifacts of SPL preserved throughout the SPL lifecycle?

Preservation of traceability between test assets and other artifacts is a crucial factor in SPL testing as it enhances the reusability of test assets and manages the complexity of SPL testing. However, only a few papers consider preserving traceability throughout the SPL lifecycle. The papers are categorized based on the types of artifacts associated with test assets, focusing on preserving traceability between requirements and test assets as well as between configurations and test assets. The distribution of primary studies addressing this aspect highlights that most of the studies focus on preserving traceability between requirements and test assets.

RQ6 . How are Non-Functional Requirements (NFRs) tested in SPL?

Testing NFRs in SPLs has been rarely examined by researchers, with only three studies addressing this aspect. These studies cover various categories of NFRs, such as load testing, performance profiling, NFRs at the hardware-in-the-loop level, and real-time properties.

RQ7 . What mechanisms have been used for controlling cost/effort of SPL testing?

Various techniques have been proposed to manage the cost and effort associated with SPL testing. However, the lack of a standardized classification for these techniques has made their analysis challenging. Notably, research on product sampling techniques has been extensively categorized into sub-techniques such as automatic selection, semi-automatic selection, and coverage. Beyond sampling techniques, other approaches have emerged, categorized based on their primary contributions, including reusing test assets, providing different levels of automation, handling product selection for testing, prioritizing configurations/test cases, and minimizing the test suite size.

These techniques are often combinable, as seen in the use of test prioritization and minimization techniques alongside sampling techniques to optimize testing cost and effort further. Moreover, the list of techniques continues to evolve with new publications on SPL testing. The distribution of studies reveals that the majority focus on proposing a specific level of automation (∼ 62%). However, many studies lack details on the specific tools used for this purpose. The second most researched category involves handling the selection of products to test (∼ 39%). Additionally, techniques related to reusing test assets (∼ 25%), prioritizing configurations/test cases (∼ 18%), and minimizing test suite size (∼ 15%) are also explored.

We only included studies empirically evaluated in our analysis. In this discussion, we emphasize the maturity of evaluations conducted in these studies, highlight the contributions of the studies in addressing the research questions, present the main findings, and propose research directions to address identified gaps. It is important to note that our SLR intentionally focused exclusively on SPLs. We deliberately excluded the broader context of configurable systems from our analysis to have a clear focus for our article. Therefore, all the findings and research gaps reported in this section are based on our analysis within the SPL testing area. We acknowledge that this might lead to missing synergies with contributions from the broader field of configurable systems. Still, we hope this SLR can be the basis for exploring these aspects in future work.

6.1 Overview of evaluation maturity and studies’ contributions

Proposed approaches have been evaluated using three types of evaluation methods, including case studies, experiments, and expert surveys. However, there is variation in the scope and type of SPLs employed in these evaluations. Different types of SPLs have been employed in the evaluations, representing diverse application domains, such as embedded systems (e.g., automotive and medical systems), web-based systems, banking systems, and smartphone and vendor machine SPLs. We categorized the scope of applications employed in the evaluations into three main groups: Industrial systems with real data sets, SPLs sourced from online repositories (e.g., SPLOT repository) or extracted from existing sources, and the development of a demonstrator. It is important to note that some studies utilized more than one category of applications, for instance, both industrial SPLs and SPLs available online. Approximately 60% of the studies (71 studies) conducted evaluations using SPLs available online or derived from prior research. Around 17% (20 studies) involved the development of a demonstrator for assessing the proposed approach. Only 29% (34 studies) utilized an industrial-scale SPL (Industrial study or Industrial practice) for evaluating their approach. This issue may jeopardize the adoption of the proposed approaches in industry; therefore, proposed approaches for SPL testing need to improve from their evaluation perspective.

Discussing threats to validity is crucial in research since it helps researchers and readers understand the limitations and potential challenges associated with the study. However, an analysis of the included studies reveals that only 32 primary studies (∼ 27%) extensively discussed threats to validity. In approximately 42 studies (∼ 36%), the examination of threats to validity was brief. Notably, 44 studies (∼ 37%) entirely neglected to address this crucial aspect.

Another aspect that is worth analyzing is the distribution of the studies based on their contribution to the research questions. Figure  3 represents the frequencies of studies according to the research questions addressed by them. It should be mentioned that some studies covered more than one topic; therefore, the total amount shown in Fig.  3 exceeds the total number of studies selected for final analysis. As seen in Fig.  3 , most studies address the questions RQ7 (Controlling cost/effort of SPL testing) and RQ2 (Test levels in SPL testing). Moreover, there is notable research interest in the area of configuration-aware testing (RQ4), followed by a substantial focus on variability-aware creation of test assets (RQ3). However, some aspects of SPL testing have rarely been considered and, therefore, need new solutions, including RQ5 (Traceability between test assets and other artifacts) and RQ6 (Non-functional testing).

Distribution of studies by the contribution to the research questions

6.2 Main findings

We analyzed the data based on the content structuring/theme analysis approach of Mayring ( 2014 ). Initially, the data extracted from the extraction form provided us with a list of key challenges and sub-themes. In the next step, we inductively created categories within the themes to summarize them (analytical themes). The results of this analysis are shown in Table  11 . In the rest of this section, we present various gaps and concerns that necessitate further exploration and attention from both researchers and practitioners:

Variability management : Effective variability management in SPLs is crucial, yet it introduces complexities that can pose challenges to testing (Sect.  4.3 ). One facet that needs further exploration is the challenges associated with variability control. It demands a more in-depth investigation to identify and analyze challenges arising from the diverse features and configurations inherent in SPLs. These challenges encompass the complexities introduced by numerous potential combinations and the possibility of unforeseen interactions among variable elements. While this aspect has been previously examined, the key concern lies in the applicability of the proposed solutions and approaches in real-world scenarios. For example, one of the most investigated solutions involves selecting a subset of products for testing. However, the potential for unseen interactions between features in new products to result in faults raises doubts. Furthermore, many of the proposed approaches have only been evaluated at a proof-of-concept level, necessitating a more in-depth investigation into their suitability for industrial SPL applications.

Another crucial aspect involves examining variability modeling. This includes an analysis of the current state of variability modeling in SPL testing and an exploration of opportunities to enhance modeling techniques to address testing challenges. While model-based approaches, commonly used to create variant-rich test assets, have shown promise in SPL testing, there is still room for improvement in automating the generation of test cases and ensuring comprehensive coverage based on variability models. Utilizing model-based approaches can automate the process of transforming high-level test assets (e.g., test scenarios) and generating low-level test assets (e.g., test cases and test data).

Non-functional testing : Despite the fact that functional testing of SPLs has been extensively investigated, non-functional testing aspects need greater focus and specific methodologies (Sect.  4.6 ). This particular gap has already been acknowledged in previous literature reviews. Non-functional requirements encompass diverse dimensions, including but not limited to performance, security, usability, and scalability. While some studies have explored aspects such as real-time behaviors and performance, there remains a need for further research to comprehensively address diverse facets within this domain. Moreover, the inherent nature of non-functional requirements significantly shapes testing strategies. Considering their distinct characteristics and evaluation criteria, it is crucial to investigate how distinct testing approaches are essential for various aspects like performance testing, security testing, and usability testing.

Non-functional testing, particularly in critical areas such as performance and security, poses challenges due to its resource-intensive nature. Investigating the challenges associated with acquiring and allocating resources for thorough non-functional testing throughout the SPL lifecycle is crucial for effective quality assurance.

The complexities of seamlessly integrating non-functional testing with functional testing necessitate further exploration. Examining how the interplay between these two testing dimensions influences the overall quality assurance process will contribute valuable insights to the field.

Tool support : Given the substantial testing effort required for SPLs, the availability of tools specifically designed for SPL testing is crucial (Sect.  4.7 ). The analysis of the studies with respect to automation provided by the tools indicates that most of the tool implementations are proof-of-concept prototypes developed for validating the proposed approach. Therefore, developing more robust and user-friendly tools can significantly help practitioners in their testing efforts. This particular challenge has previously been discussed in prior literature reviews.

Some specific areas need further exploration. Evaluating the effectiveness and efficiency of existing SPL testing tools explores capabilities, limitations, and areas for improvement in tools designed for various testing activities within the SPL lifecycle. Analyzing how well testing tools adapt to changes in SPL configurations includes investigating their ability to accommodate evolving feature sets, configurations, and architectural variations, ensuring continued effectiveness. Assessing the user experience and usability of SPL testing tools explores how user-friendly and accessible tools are for practitioners involved in SPL testing, considering factors such as ease of use, learning curve, and user satisfaction.

Regression testing : Effectively handling regression testing in SPLs, where modifications to one product can affect others, presents an intricate challenge (Sect.  4.7 ). Regression test selection/prioritization/minimization and architecture-based regression testing are potential points for future research. Test case selection is focused on choosing a set of relevant test cases to test the modified version of the system, and the aim of test minimization is to remove the redundant/irrelevant test cases from the existing test suit. Test case prioritization aims at ordering and ranking test cases based on specific criteria such as importance and likelihood of failure. All these techniques aim to reduce the cost/effort of SPL testing after applying any change to products or the SPL architecture.

An important aspect is analyzing how changes and evolutions in the SPL architecture impact regression testing strategies. This investigation includes understanding the challenges of maintaining test suites across evolving SPL configurations and the need for adaptive regression testing approaches.

Additionally, exploring the benefits and challenges of implementing automated regression testing within the SPL context is crucial. This requires an analysis of efficiency gains, potential pitfalls, and strategies to optimize the effectiveness of automated regression testing in SPL scenarios.

Moreover, investigating challenges related to maintaining traceability between evolving codebase versions and regression test suites is critical. This requires exploring strategies to preserve traceability links, ensuring that regression testing aligns with the dynamic nature of SPL development.

Industrial evaluations : Encouraging the adoption of SPL testing practices in industrial settings requires addressing practical challenges (Sect.  3.3 and 4.1 ). This includes offering guidance tailored for industry-specific SPL testing and conducting industrial evaluations.

To enhance the industry adoption of SPL approaches, offering practical insights and recommendations is essential. This involves providing tailored guidance to help organizations navigate the unique challenges and requirements of adopting SPL testing methods in their specific industry domains. Additionally, there is a need to move beyond proof-of-concept evaluations and conduct practical assessments to verify the feasibility, scalability, and effectiveness of proposed SPL testing methods in diverse industrial contexts.

Test levels throughout the SPL lifecycle : Exploring the details of a test level throughout the SPL lifecycle and illustrating the challenges associated with neglecting a particular test level would provide valuable insights for practitioners (Sect.  4.2 ). Two levels of tests are commonly executed throughout Domain Engineering: Unit testing and Integration testing. Although testing common core assets of an SPL is vital to detect faults as soon as possible, a few studies have considered the execution of tests in domain engineering. Therefore, it would be useful to conduct further investigations regarding how to execute a specific level of test in Domain Engineering and the consequences of not performing it. In Application Engineering, three levels of tests are usually executed: Unit testing, Integration testing, and System/acceptance testing. The two last levels have been investigated in most of the studies. It is worth mentioning that Unit testing has been investigated as a level of test in Application Engineering in a few studies published in recent years. In contrast, previous literature reviews have not reported this level of test in Application Engineering (e.g., Pérez et al. 2009 ). This indicates no consensus on the test levels executed during Domain Engineering and Application Engineering.

Another aspect that needs further exploration involves examining the influence of variabilities inherent in SPLs on different test levels. This requires understanding how the presence of variable features across products affects test activities, including planning, design, and execution at each testing level. Additionally, there is a need to investigate how test levels adapt to requirements and feature set changes throughout the SPL lifecycle. This requires exploring the challenges and opportunities associated with maintaining effective testing strategies in response to the dynamic nature of evolving product configurations.

Preserving the traceability between test assets and development artifacts : Preserving traceability between test assets and development artifacts in SPLs is particularly challenging due to the complex relationships between product variants and the shared assets (Sect.  4.5 ). Studies that target testing SPLs (very) rarely consider traceability explicitly. Examining the challenges associated with preserving traceability is crucial, especially when dealing with evolving product configurations within the SPL testing environment. While researchers have proposed certain methods, such as Reis et al. ( 2007 ) which preserved the traceability between requirements and test case scenarios using UML models and by refining use case scenarios into test case scenarios, Reuys et al. ( 2006 ) enabled traceability between artifacts, there remains a necessity to investigate more efficient approaches for modeling and representing traceability relationships, considering feature variability and configuration management. Furthermore, exploring the creation of automated tools and techniques for establishing and consistently updating traceability links in response to the evolving nature of SPLs presents an engaging area for future research.

To compare findings with previous SLRs, Table  12 presents a summary of the findings from both the current study and prior literature reviews (Pérez et al. 2009 ; Engström and Runeson 2011 ; Da Mota Silveira Neto et al. 2011 ; do Carmo Machado et al. 2014 ).

7 Related work

This research aims to provide researchers and practitioners with an overview of state-of-the-art testing practices applied to SPL and identify the gaps between required techniques and existing approaches. Accordingly, we conducted an SLR to analyze existing approaches to SPL testing. Therefore, SLRs and SMSs on SPL testing can be considered as works related to this research. To the best of our knowledge, four papers have systematically analyzed approaches focused on SPL testing (Pérez et al. 2009 ; Engström and Runeson 2011 ; Da Mota Silveira Neto et al. 2011 ; do Carmo Machado et al. 2014 ).

Pérez et al. ( 2009 ) conducted an SLR to identify experience reports and initiatives carried out in the SPL testing area. In this work, primary studies were classified into seven categories: Unit testing, Integration testing, functional testing, SPL Architecture testing, Embedded system testing, testing process and testing effort in SPL. Then, they presented a summary of each area. The similarity of this SLR to our work is testing levels investigated in both works; however, our work is broader in scope than this SLR since we investigated more aspects of SPL testing.

Engström and Runeson ( 2011 ) conducted an SMS by analyzing papers published up to 2008. The authors mapped studies into seven categories based on their research focus: Test organization and process, Test management, Testability, System and acceptance testing, Integration testing, Unit testing, and Test automation. They also identified challenges in SPL testing and needs for future research. This SMS has similarities with our work regarding specific SPL aspects investigated, including testing levels and test automation. However, the research questions designed by Engström and Runeson ( 2011 ) are more general, focusing on specifying challenges and topics investigated in SPL testing.

Da Mota Silveira Neto et al. ( 2011 ) conducted an SMS to investigate state-of-the-art testing practices by analyzing a set of 45 publications dated from 1993 to 2009. Primary studies are mapped into nine categories: Testing strategy, Static and dynamic analysis, Testing levels, Regression testing, Non-functional testing, Commonality and variability testing, Variant binding time, Effort reduction, and Test measurement. Some of the research questions designed by Da Mota Silveira Neto et al. ( 2011 ) are similar to the ones investigated in our work (e.g., testing SPLs while considering commonalities and variabilities). However, our work is broader in scope since we analyzed 110 papers published up to 2022. Furthermore, we only included empirically evaluated studies in our review.

do Carmo Machado et al. ( 2014 ) conducted an SLR by analyzing 49 studies published up to 2013; this SLR aimed to identify testing strategies that could achieve higher defect detection rates and reduced quality assurance effort. Identifying strategies to handle the selection of products to test has been investigated in both (do Carmo Machado et al. 2014 ) and our work. Furthermore, similar to our work, the initial set of primary studies in study (do Carmo Machado et al. 2014 ) has been identified by investigating previously conducted SLRs or SMSs, published up to the year 2009; also, the authors of this SLR only included empirically evaluated studies. However, our work investigates more aspects of SPL testing (e.g., preserving traceability between test assets and other artifacts) and analyzes more studies (110 papers).

Literature reviews also specifically focused on analyzing one aspect of SPL testing. As an example, Lopez-Herrejon et al. ( 2015 ) conducted an SMS to identify techniques that have been applied for combinatorial interaction testing of SPLs. However, our work is broader in scope since we did not limit the studies to a specific technique.

In general, the previous literature reviews and our work complement each other regarding the research questions addressed. Some aspects of SPL testing have not been considered in detail in previous reviews: techniques used for preserving traceability between test artifacts and other artifacts, techniques employed for identifying valid and invalid configurations, and different ways to control cost/effort of SPL testing were not covered in an extent that makes it possible to identify the current status of research and practice from the perspective of those aspects.

8 Conclusions and future work

The goal of SPLE is to improve the effectiveness and efficiency of software development by managing commonalities and variabilities among products. Testing is an essential part of SPLE to achieve the benefits of an SPL. It is focused on detecting potential faults in core assets created during Domain Engineering and products created during Application Engineering by reusing core assets. This paper presents the results of a systematic literature review of testing in SPLE. The SLR aimed to investigate specific aspects of SPL testing that were formulated as seven research questions, identify gaps, and address specific points of SPLE that still need to be fully addressed.

The analysis that we conducted based on 118 studies from 2003 to 2022 has uncovered a range of issues and considerations that researchers and practitioners can work on. It is shown that managing variability in SPL testing is vital but can complicate the testing process. Model-based methods show promise in generating test assets, but there is room for improvement in automating test case creation and ensuring comprehensive coverage. Non-functional testing aspects like performance, security, and usability require more attention and specific methodologies. Having the right tools is important, but most tool implementations are still in the proof-of-concept stage. Regression testing poses a complex challenge, and future research should concentrate on areas like regression test selection, prioritization, minimization, and architecture-based regression testing. Establishing benchmark datasets and standard evaluation criteria for SPL testing methods would simplify comparing and adopting various techniques.

Exploring test levels throughout the SPL lifecycle and illustrating the challenges of neglecting a particular test level would offer valuable insights. Additionally, studies focusing on testing SPLs need to address traceability explicitly. Maintaining traceability between test assets and development artifacts is especially difficult due to the intricate relationships between product variants and shared assets, which requires effective approaches. It is also worth mentioning that, throughout selecting studies for final analysis, we included only the studies empirically evaluated. By analyzing the evaluation conducted in the studies, we noticed that most of the studies were assessed by applying only one empirical method. Furthermore, most of the assessments undertaken do not refer to real-world practice. This indicates the need to evaluate SPL testing approaches not in academia but in industry.

Based on the findings of this SLR, further research in the SPL testing field can be expended on specific areas we identified throughout this research as the potential points for future research (e.g., SPL regression testing). Furthermore, empirical assessment of existing techniques for the investigated aspects (e.g., selection of products to test or creating reusable test assets) to compare those techniques would be helpful for both researchers and practitioners, mainly if those techniques are applied to real-world and large-scale scenarios. Furthermore, this research can be strengthened by examining studies published in the field of testing configurable systems. Such analysis can investigate how techniques from this broader domain might be applied to SPL testing to address existing deficiencies in this area.

Data availability

All data generated during this study are available in the “Zenodo” repository: https://zenodo.org/doi/10.5281/zenodo.10018266 .

Replication package available on https://zenodo.org/doi/ https://doi.org/10.5281/zenodo.10018266 .

SPLC stands for Software Product Line Conference.

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Table  17 shows the results of the evaluation based on the quality assessment criteria, described in Table  14 in Appendix B. Regarding the issue Reporting (QA1-QA3 in Table  14 ), most of the studies performed well; all the studies are based on research and almost 82% of them have a clear statement of the aims of the research. However, the description of the context is bad in some of the studies (∼ 30%); this compromises the validity of these studies since, without enough information about the subjects of the study, it is usually difficult to specify whether the selected case is suitable to evaluate different aspects of the proposed approach.

In terms of rigor (QA4-QA7), the studies performed, on average, fairly well. In 77 studies (∼ 62%), the researchers have justified the research design so that it can address the aims of the research. In 71 studies (∼ 60%), the proposed approach has been compared with a base approach; the researcher(s) has tried to justify that the selected controls are representative of a defined population. The way data collected is satisfactory in 85 studies (∼ 72%) since the researchers have clearly defined the measure(s) selected and justified their selection. Furthermore, the data has been analyzed rigorously in 80 studies (68%) by providing sufficient data to support the findings. Although these findings are promising, 32% of the studies, overall, fail in rigor; this compromises the validity and usefulness of these studies since failing in rigor, as a key issue in Evidence-Based Software Engineering, indicates that the empirical methods have been applied in an informal way.

Regarding the issue Credibility, 95% of the studies provide a clear statement of the findings (QA9) by discussing the findings in relation to the research questions and also presenting the limitations of the study. However, most studies perform poorly in establishing relationships between the researcher(s) and participants and the data collected to address the research issue (QA8); this quality attribute is considered in only 12 studies (∼ 10%). This can threaten the quality of the research due to not considering potential bias and influence of the researcher(s) during the formulation of research questions, data collection, and analysis and selection of data for presentation.

In terms of Relevance, 114 studies (∼ 97%) explicitly deal with SPL testing and discuss the contributions the study makes to existing knowledge, identify new areas in which research is necessary, and discuss the ways in which the research can be used (QA10). This result is in line with the nature of the research goals, described as inclusion and exclusion criteria in Sect. 3.2. However, only 18 studies (∼ 15%) present practitioner-based guidelines (QA11). This indicates that the SPL testing field needs more practical guidance to strengthen the adoption of industry.

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Agh, H., Azamnouri, A. & Wagner, S. Software product line testing: a systematic literature review. Empir Software Eng 29 , 146 (2024). https://doi.org/10.1007/s10664-024-10516-x

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