case study examples for computer science students

10 Real World Data Science Case Studies Projects with Example

Top 10 Data Science Case Studies Projects with Examples and Solutions in Python to inspire your data science learning in 2023.

BelData science has been a trending buzzword in recent times. With wide applications in various sectors like healthcare , education, retail, transportation, media, and banking -data science applications are at the core of pretty much every industry out there. The possibilities are endless: analysis of frauds in the finance sector or the personalization of recommendations on eCommerce businesses.  We have developed ten exciting data science case studies to explain how data science is leveraged across various industries to make smarter decisions and develop innovative personalized products tailored to specific customers.

Walmart Sales Forecasting Data Science Project

Downloadable solution code | Explanatory videos | Tech Support

Table of Contents

Data science case studies in retail , data science case study examples in entertainment industry , data analytics case study examples in travel industry , case studies for data analytics in social media , real world data science projects in healthcare, data analytics case studies in oil and gas, what is a case study in data science, how do you prepare a data science case study, 10 most interesting data science case studies with examples.

So, without much ado, let's get started with data science business case studies !

With humble beginnings as a simple discount retailer, today, Walmart operates in 10,500 stores and clubs in 24 countries and eCommerce websites, employing around 2.2 million people around the globe. For the fiscal year ended January 31, 2021, Walmart's total revenue was $559 billion showing a growth of $35 billion with the expansion of the eCommerce sector. Walmart is a data-driven company that works on the principle of 'Everyday low cost' for its consumers. To achieve this goal, they heavily depend on the advances of their data science and analytics department for research and development, also known as Walmart Labs. Walmart is home to the world's largest private cloud, which can manage 2.5 petabytes of data every hour! To analyze this humongous amount of data, Walmart has created 'Data Café,' a state-of-the-art analytics hub located within its Bentonville, Arkansas headquarters. The Walmart Labs team heavily invests in building and managing technologies like cloud, data, DevOps , infrastructure, and security.

Walmart is experiencing massive digital growth as the world's largest retailer . Walmart has been leveraging Big data and advances in data science to build solutions to enhance, optimize and customize the shopping experience and serve their customers in a better way. At Walmart Labs, data scientists are focused on creating data-driven solutions that power the efficiency and effectiveness of complex supply chain management processes. Here are some of the applications of data science  at Walmart:

i) Personalized Customer Shopping Experience

Walmart analyses customer preferences and shopping patterns to optimize the stocking and displaying of merchandise in their stores. Analysis of Big data also helps them understand new item sales, make decisions on discontinuing products, and the performance of brands.

ii) Order Sourcing and On-Time Delivery Promise

Millions of customers view items on Walmart.com, and Walmart provides each customer a real-time estimated delivery date for the items purchased. Walmart runs a backend algorithm that estimates this based on the distance between the customer and the fulfillment center, inventory levels, and shipping methods available. The supply chain management system determines the optimum fulfillment center based on distance and inventory levels for every order. It also has to decide on the shipping method to minimize transportation costs while meeting the promised delivery date.

Here's what valued users are saying about ProjectPro

Ameeruddin Mohammed

ETL (Abintio) developer at IBM

Tech Leader | Stanford / Yale University

Not sure what you are looking for?

iii) Packing Optimization 

Also known as Box recommendation is a daily occurrence in the shipping of items in retail and eCommerce business. When items of an order or multiple orders for the same customer are ready for packing, Walmart has developed a recommender system that picks the best-sized box which holds all the ordered items with the least in-box space wastage within a fixed amount of time. This Bin Packing problem is a classic NP-Hard problem familiar to data scientists .

Whenever items of an order or multiple orders placed by the same customer are picked from the shelf and are ready for packing, the box recommendation system determines the best-sized box to hold all the ordered items with a minimum of in-box space wasted. This problem is known as the Bin Packing Problem, another classic NP-Hard problem familiar to data scientists.

Here is a link to a sales prediction data science case study to help you understand the applications of Data Science in the real world. Walmart Sales Forecasting Project uses historical sales data for 45 Walmart stores located in different regions. Each store contains many departments, and you must build a model to project the sales for each department in each store. This data science case study aims to create a predictive model to predict the sales of each product. You can also try your hands-on Inventory Demand Forecasting Data Science Project to develop a machine learning model to forecast inventory demand accurately based on historical sales data.

Get Closer To Your Dream of Becoming a Data Scientist with 70+ Solved End-to-End ML Projects

Amazon is an American multinational technology-based company based in Seattle, USA. It started as an online bookseller, but today it focuses on eCommerce, cloud computing , digital streaming, and artificial intelligence . It hosts an estimate of 1,000,000,000 gigabytes of data across more than 1,400,000 servers. Through its constant innovation in data science and big data Amazon is always ahead in understanding its customers. Here are a few data analytics case study examples at Amazon:

i) Recommendation Systems

Data science models help amazon understand the customers' needs and recommend them to them before the customer searches for a product; this model uses collaborative filtering. Amazon uses 152 million customer purchases data to help users to decide on products to be purchased. The company generates 35% of its annual sales using the Recommendation based systems (RBS) method.

Here is a Recommender System Project to help you build a recommendation system using collaborative filtering. 

ii) Retail Price Optimization

Amazon product prices are optimized based on a predictive model that determines the best price so that the users do not refuse to buy it based on price. The model carefully determines the optimal prices considering the customers' likelihood of purchasing the product and thinks the price will affect the customers' future buying patterns. Price for a product is determined according to your activity on the website, competitors' pricing, product availability, item preferences, order history, expected profit margin, and other factors.

Check Out this Retail Price Optimization Project to build a Dynamic Pricing Model.

iii) Fraud Detection

Being a significant eCommerce business, Amazon remains at high risk of retail fraud. As a preemptive measure, the company collects historical and real-time data for every order. It uses Machine learning algorithms to find transactions with a higher probability of being fraudulent. This proactive measure has helped the company restrict clients with an excessive number of returns of products.

You can look at this Credit Card Fraud Detection Project to implement a fraud detection model to classify fraudulent credit card transactions.

New Projects

Let us explore data analytics case study examples in the entertainment indusry.

Ace Your Next Job Interview with Mock Interviews from Experts to Improve Your Skills and Boost Confidence!

Netflix started as a DVD rental service in 1997 and then has expanded into the streaming business. Headquartered in Los Gatos, California, Netflix is the largest content streaming company in the world. Currently, Netflix has over 208 million paid subscribers worldwide, and with thousands of smart devices which are presently streaming supported, Netflix has around 3 billion hours watched every month. The secret to this massive growth and popularity of Netflix is its advanced use of data analytics and recommendation systems to provide personalized and relevant content recommendations to its users. The data is collected over 100 billion events every day. Here are a few examples of data analysis case studies applied at Netflix :

i) Personalized Recommendation System

Netflix uses over 1300 recommendation clusters based on consumer viewing preferences to provide a personalized experience. Some of the data that Netflix collects from its users include Viewing time, platform searches for keywords, Metadata related to content abandonment, such as content pause time, rewind, rewatched. Using this data, Netflix can predict what a viewer is likely to watch and give a personalized watchlist to a user. Some of the algorithms used by the Netflix recommendation system are Personalized video Ranking, Trending now ranker, and the Continue watching now ranker.

ii) Content Development using Data Analytics

Netflix uses data science to analyze the behavior and patterns of its user to recognize themes and categories that the masses prefer to watch. This data is used to produce shows like The umbrella academy, and Orange Is the New Black, and the Queen's Gambit. These shows seem like a huge risk but are significantly based on data analytics using parameters, which assured Netflix that they would succeed with its audience. Data analytics is helping Netflix come up with content that their viewers want to watch even before they know they want to watch it.

iii) Marketing Analytics for Campaigns

Netflix uses data analytics to find the right time to launch shows and ad campaigns to have maximum impact on the target audience. Marketing analytics helps come up with different trailers and thumbnails for other groups of viewers. For example, the House of Cards Season 5 trailer with a giant American flag was launched during the American presidential elections, as it would resonate well with the audience.

Here is a Customer Segmentation Project using association rule mining to understand the primary grouping of customers based on various parameters.

Get FREE Access to Machine Learning Example Codes for Data Cleaning , Data Munging, and Data Visualization

In a world where Purchasing music is a thing of the past and streaming music is a current trend, Spotify has emerged as one of the most popular streaming platforms. With 320 million monthly users, around 4 billion playlists, and approximately 2 million podcasts, Spotify leads the pack among well-known streaming platforms like Apple Music, Wynk, Songza, amazon music, etc. The success of Spotify has mainly depended on data analytics. By analyzing massive volumes of listener data, Spotify provides real-time and personalized services to its listeners. Most of Spotify's revenue comes from paid premium subscriptions. Here are some of the examples of case study on data analytics used by Spotify to provide enhanced services to its listeners:

i) Personalization of Content using Recommendation Systems

Spotify uses Bart or Bayesian Additive Regression Trees to generate music recommendations to its listeners in real-time. Bart ignores any song a user listens to for less than 30 seconds. The model is retrained every day to provide updated recommendations. A new Patent granted to Spotify for an AI application is used to identify a user's musical tastes based on audio signals, gender, age, accent to make better music recommendations.

Spotify creates daily playlists for its listeners, based on the taste profiles called 'Daily Mixes,' which have songs the user has added to their playlists or created by the artists that the user has included in their playlists. It also includes new artists and songs that the user might be unfamiliar with but might improve the playlist. Similar to it is the weekly 'Release Radar' playlists that have newly released artists' songs that the listener follows or has liked before.

ii) Targetted marketing through Customer Segmentation

With user data for enhancing personalized song recommendations, Spotify uses this massive dataset for targeted ad campaigns and personalized service recommendations for its users. Spotify uses ML models to analyze the listener's behavior and group them based on music preferences, age, gender, ethnicity, etc. These insights help them create ad campaigns for a specific target audience. One of their well-known ad campaigns was the meme-inspired ads for potential target customers, which was a huge success globally.

iii) CNN's for Classification of Songs and Audio Tracks

Spotify builds audio models to evaluate the songs and tracks, which helps develop better playlists and recommendations for its users. These allow Spotify to filter new tracks based on their lyrics and rhythms and recommend them to users like similar tracks ( collaborative filtering). Spotify also uses NLP ( Natural language processing) to scan articles and blogs to analyze the words used to describe songs and artists. These analytical insights can help group and identify similar artists and songs and leverage them to build playlists.

Here is a Music Recommender System Project for you to start learning. We have listed another music recommendations dataset for you to use for your projects: Dataset1 . You can use this dataset of Spotify metadata to classify songs based on artists, mood, liveliness. Plot histograms, heatmaps to get a better understanding of the dataset. Use classification algorithms like logistic regression, SVM, and Principal component analysis to generate valuable insights from the dataset.

Explore Categories

Below you will find case studies for data analytics in the travel and tourism industry.

Airbnb was born in 2007 in San Francisco and has since grown to 4 million Hosts and 5.6 million listings worldwide who have welcomed more than 1 billion guest arrivals in almost every country across the globe. Airbnb is active in every country on the planet except for Iran, Sudan, Syria, and North Korea. That is around 97.95% of the world. Using data as a voice of their customers, Airbnb uses the large volume of customer reviews, host inputs to understand trends across communities, rate user experiences, and uses these analytics to make informed decisions to build a better business model. The data scientists at Airbnb are developing exciting new solutions to boost the business and find the best mapping for its customers and hosts. Airbnb data servers serve approximately 10 million requests a day and process around one million search queries. Data is the voice of customers at AirBnB and offers personalized services by creating a perfect match between the guests and hosts for a supreme customer experience. 

i) Recommendation Systems and Search Ranking Algorithms

Airbnb helps people find 'local experiences' in a place with the help of search algorithms that make searches and listings precise. Airbnb uses a 'listing quality score' to find homes based on the proximity to the searched location and uses previous guest reviews. Airbnb uses deep neural networks to build models that take the guest's earlier stays into account and area information to find a perfect match. The search algorithms are optimized based on guest and host preferences, rankings, pricing, and availability to understand users’ needs and provide the best match possible.

ii) Natural Language Processing for Review Analysis

Airbnb characterizes data as the voice of its customers. The customer and host reviews give a direct insight into the experience. The star ratings alone cannot be an excellent way to understand it quantitatively. Hence Airbnb uses natural language processing to understand reviews and the sentiments behind them. The NLP models are developed using Convolutional neural networks .

Practice this Sentiment Analysis Project for analyzing product reviews to understand the basic concepts of natural language processing.

iii) Smart Pricing using Predictive Analytics

The Airbnb hosts community uses the service as a supplementary income. The vacation homes and guest houses rented to customers provide for rising local community earnings as Airbnb guests stay 2.4 times longer and spend approximately 2.3 times the money compared to a hotel guest. The profits are a significant positive impact on the local neighborhood community. Airbnb uses predictive analytics to predict the prices of the listings and help the hosts set a competitive and optimal price. The overall profitability of the Airbnb host depends on factors like the time invested by the host and responsiveness to changing demands for different seasons. The factors that impact the real-time smart pricing are the location of the listing, proximity to transport options, season, and amenities available in the neighborhood of the listing.

Here is a Price Prediction Project to help you understand the concept of predictive analysis which is widely common in case studies for data analytics. 

Uber is the biggest global taxi service provider. As of December 2018, Uber has 91 million monthly active consumers and 3.8 million drivers. Uber completes 14 million trips each day. Uber uses data analytics and big data-driven technologies to optimize their business processes and provide enhanced customer service. The Data Science team at uber has been exploring futuristic technologies to provide better service constantly. Machine learning and data analytics help Uber make data-driven decisions that enable benefits like ride-sharing, dynamic price surges, better customer support, and demand forecasting. Here are some of the real world data science projects used by uber:

i) Dynamic Pricing for Price Surges and Demand Forecasting

Uber prices change at peak hours based on demand. Uber uses surge pricing to encourage more cab drivers to sign up with the company, to meet the demand from the passengers. When the prices increase, the driver and the passenger are both informed about the surge in price. Uber uses a predictive model for price surging called the 'Geosurge' ( patented). It is based on the demand for the ride and the location.

ii) One-Click Chat

Uber has developed a Machine learning and natural language processing solution called one-click chat or OCC for coordination between drivers and users. This feature anticipates responses for commonly asked questions, making it easy for the drivers to respond to customer messages. Drivers can reply with the clock of just one button. One-Click chat is developed on Uber's machine learning platform Michelangelo to perform NLP on rider chat messages and generate appropriate responses to them.

iii) Customer Retention

Failure to meet the customer demand for cabs could lead to users opting for other services. Uber uses machine learning models to bridge this demand-supply gap. By using prediction models to predict the demand in any location, uber retains its customers. Uber also uses a tier-based reward system, which segments customers into different levels based on usage. The higher level the user achieves, the better are the perks. Uber also provides personalized destination suggestions based on the history of the user and their frequently traveled destinations.

You can take a look at this Python Chatbot Project and build a simple chatbot application to understand better the techniques used for natural language processing. You can also practice the working of a demand forecasting model with this project using time series analysis. You can look at this project which uses time series forecasting and clustering on a dataset containing geospatial data for forecasting customer demand for ola rides.

Explore More  Data Science and Machine Learning Projects for Practice. Fast-Track Your Career Transition with ProjectPro

7) LinkedIn 

LinkedIn is the largest professional social networking site with nearly 800 million members in more than 200 countries worldwide. Almost 40% of the users access LinkedIn daily, clocking around 1 billion interactions per month. The data science team at LinkedIn works with this massive pool of data to generate insights to build strategies, apply algorithms and statistical inferences to optimize engineering solutions, and help the company achieve its goals. Here are some of the real world data science projects at LinkedIn:

i) LinkedIn Recruiter Implement Search Algorithms and Recommendation Systems

LinkedIn Recruiter helps recruiters build and manage a talent pool to optimize the chances of hiring candidates successfully. This sophisticated product works on search and recommendation engines. The LinkedIn recruiter handles complex queries and filters on a constantly growing large dataset. The results delivered have to be relevant and specific. The initial search model was based on linear regression but was eventually upgraded to Gradient Boosted decision trees to include non-linear correlations in the dataset. In addition to these models, the LinkedIn recruiter also uses the Generalized Linear Mix model to improve the results of prediction problems to give personalized results.

ii) Recommendation Systems Personalized for News Feed

The LinkedIn news feed is the heart and soul of the professional community. A member's newsfeed is a place to discover conversations among connections, career news, posts, suggestions, photos, and videos. Every time a member visits LinkedIn, machine learning algorithms identify the best exchanges to be displayed on the feed by sorting through posts and ranking the most relevant results on top. The algorithms help LinkedIn understand member preferences and help provide personalized news feeds. The algorithms used include logistic regression, gradient boosted decision trees and neural networks for recommendation systems.

iii) CNN's to Detect Inappropriate Content

To provide a professional space where people can trust and express themselves professionally in a safe community has been a critical goal at LinkedIn. LinkedIn has heavily invested in building solutions to detect fake accounts and abusive behavior on their platform. Any form of spam, harassment, inappropriate content is immediately flagged and taken down. These can range from profanity to advertisements for illegal services. LinkedIn uses a Convolutional neural networks based machine learning model. This classifier trains on a training dataset containing accounts labeled as either "inappropriate" or "appropriate." The inappropriate list consists of accounts having content from "blocklisted" phrases or words and a small portion of manually reviewed accounts reported by the user community.

Here is a Text Classification Project to help you understand NLP basics for text classification. You can find a news recommendation system dataset to help you build a personalized news recommender system. You can also use this dataset to build a classifier using logistic regression, Naive Bayes, or Neural networks to classify toxic comments.

Get confident to build end-to-end projects

Access to a curated library of 250+ end-to-end industry projects with solution code, videos and tech support.

Pfizer is a multinational pharmaceutical company headquartered in New York, USA. One of the largest pharmaceutical companies globally known for developing a wide range of medicines and vaccines in disciplines like immunology, oncology, cardiology, and neurology. Pfizer became a household name in 2010 when it was the first to have a COVID-19 vaccine with FDA. In early November 2021, The CDC has approved the Pfizer vaccine for kids aged 5 to 11. Pfizer has been using machine learning and artificial intelligence to develop drugs and streamline trials, which played a massive role in developing and deploying the COVID-19 vaccine. Here are a few data analytics case studies by Pfizer :

i) Identifying Patients for Clinical Trials

Artificial intelligence and machine learning are used to streamline and optimize clinical trials to increase their efficiency. Natural language processing and exploratory data analysis of patient records can help identify suitable patients for clinical trials. These can help identify patients with distinct symptoms. These can help examine interactions of potential trial members' specific biomarkers, predict drug interactions and side effects which can help avoid complications. Pfizer's AI implementation helped rapidly identify signals within the noise of millions of data points across their 44,000-candidate COVID-19 clinical trial.

ii) Supply Chain and Manufacturing

Data science and machine learning techniques help pharmaceutical companies better forecast demand for vaccines and drugs and distribute them efficiently. Machine learning models can help identify efficient supply systems by automating and optimizing the production steps. These will help supply drugs customized to small pools of patients in specific gene pools. Pfizer uses Machine learning to predict the maintenance cost of equipment used. Predictive maintenance using AI is the next big step for Pharmaceutical companies to reduce costs.

iii) Drug Development

Computer simulations of proteins, and tests of their interactions, and yield analysis help researchers develop and test drugs more efficiently. In 2016 Watson Health and Pfizer announced a collaboration to utilize IBM Watson for Drug Discovery to help accelerate Pfizer's research in immuno-oncology, an approach to cancer treatment that uses the body's immune system to help fight cancer. Deep learning models have been used recently for bioactivity and synthesis prediction for drugs and vaccines in addition to molecular design. Deep learning has been a revolutionary technique for drug discovery as it factors everything from new applications of medications to possible toxic reactions which can save millions in drug trials.

You can create a Machine learning model to predict molecular activity to help design medicine using this dataset . You may build a CNN or a Deep neural network for this data analyst case study project.

Access Data Science and Machine Learning Project Code Examples

9) Shell Data Analyst Case Study Project

Shell is a global group of energy and petrochemical companies with over 80,000 employees in around 70 countries. Shell uses advanced technologies and innovations to help build a sustainable energy future. Shell is going through a significant transition as the world needs more and cleaner energy solutions to be a clean energy company by 2050. It requires substantial changes in the way in which energy is used. Digital technologies, including AI and Machine Learning, play an essential role in this transformation. These include efficient exploration and energy production, more reliable manufacturing, more nimble trading, and a personalized customer experience. Using AI in various phases of the organization will help achieve this goal and stay competitive in the market. Here are a few data analytics case studies in the petrochemical industry:

i) Precision Drilling

Shell is involved in the processing mining oil and gas supply, ranging from mining hydrocarbons to refining the fuel to retailing them to customers. Recently Shell has included reinforcement learning to control the drilling equipment used in mining. Reinforcement learning works on a reward-based system based on the outcome of the AI model. The algorithm is designed to guide the drills as they move through the surface, based on the historical data from drilling records. It includes information such as the size of drill bits, temperatures, pressures, and knowledge of the seismic activity. This model helps the human operator understand the environment better, leading to better and faster results will minor damage to machinery used. 

ii) Efficient Charging Terminals

Due to climate changes, governments have encouraged people to switch to electric vehicles to reduce carbon dioxide emissions. However, the lack of public charging terminals has deterred people from switching to electric cars. Shell uses AI to monitor and predict the demand for terminals to provide efficient supply. Multiple vehicles charging from a single terminal may create a considerable grid load, and predictions on demand can help make this process more efficient.

iii) Monitoring Service and Charging Stations

Another Shell initiative trialed in Thailand and Singapore is the use of computer vision cameras, which can think and understand to watch out for potentially hazardous activities like lighting cigarettes in the vicinity of the pumps while refueling. The model is built to process the content of the captured images and label and classify it. The algorithm can then alert the staff and hence reduce the risk of fires. You can further train the model to detect rash driving or thefts in the future.

Here is a project to help you understand multiclass image classification. You can use the Hourly Energy Consumption Dataset to build an energy consumption prediction model. You can use time series with XGBoost to develop your model.

10) Zomato Case Study on Data Analytics

Zomato was founded in 2010 and is currently one of the most well-known food tech companies. Zomato offers services like restaurant discovery, home delivery, online table reservation, online payments for dining, etc. Zomato partners with restaurants to provide tools to acquire more customers while also providing delivery services and easy procurement of ingredients and kitchen supplies. Currently, Zomato has over 2 lakh restaurant partners and around 1 lakh delivery partners. Zomato has closed over ten crore delivery orders as of date. Zomato uses ML and AI to boost their business growth, with the massive amount of data collected over the years from food orders and user consumption patterns. Here are a few examples of data analyst case study project developed by the data scientists at Zomato:

i) Personalized Recommendation System for Homepage

Zomato uses data analytics to create personalized homepages for its users. Zomato uses data science to provide order personalization, like giving recommendations to the customers for specific cuisines, locations, prices, brands, etc. Restaurant recommendations are made based on a customer's past purchases, browsing history, and what other similar customers in the vicinity are ordering. This personalized recommendation system has led to a 15% improvement in order conversions and click-through rates for Zomato. 

You can use the Restaurant Recommendation Dataset to build a restaurant recommendation system to predict what restaurants customers are most likely to order from, given the customer location, restaurant information, and customer order history.

ii) Analyzing Customer Sentiment

Zomato uses Natural language processing and Machine learning to understand customer sentiments using social media posts and customer reviews. These help the company gauge the inclination of its customer base towards the brand. Deep learning models analyze the sentiments of various brand mentions on social networking sites like Twitter, Instagram, Linked In, and Facebook. These analytics give insights to the company, which helps build the brand and understand the target audience.

iii) Predicting Food Preparation Time (FPT)

Food delivery time is an essential variable in the estimated delivery time of the order placed by the customer using Zomato. The food preparation time depends on numerous factors like the number of dishes ordered, time of the day, footfall in the restaurant, day of the week, etc. Accurate prediction of the food preparation time can help make a better prediction of the Estimated delivery time, which will help delivery partners less likely to breach it. Zomato uses a Bidirectional LSTM-based deep learning model that considers all these features and provides food preparation time for each order in real-time. 

Data scientists are companies' secret weapons when analyzing customer sentiments and behavior and leveraging it to drive conversion, loyalty, and profits. These 10 data science case studies projects with examples and solutions show you how various organizations use data science technologies to succeed and be at the top of their field! To summarize, Data Science has not only accelerated the performance of companies but has also made it possible to manage & sustain their performance with ease.

FAQs on Data Analysis Case Studies

A case study in data science is an in-depth analysis of a real-world problem using data-driven approaches. It involves collecting, cleaning, and analyzing data to extract insights and solve challenges, offering practical insights into how data science techniques can address complex issues across various industries.

To create a data science case study, identify a relevant problem, define objectives, and gather suitable data. Clean and preprocess data, perform exploratory data analysis, and apply appropriate algorithms for analysis. Summarize findings, visualize results, and provide actionable recommendations, showcasing the problem-solving potential of data science techniques.

About the Author

ProjectPro is the only online platform designed to help professionals gain practical, hands-on experience in big data, data engineering, data science, and machine learning related technologies. Having over 270+ reusable project templates in data science and big data with step-by-step walkthroughs,

© 2024

© 2024 Iconiq Inc.

Privacy policy

User policy

Write for ProjectPro

Introduction

What is a Case Study? A cast study describes a programming problem, the process used by an expert to solve the problem, and one or more solutions to the problem. Case studies emphasize the decisions encountered by the programmer and the criteria used to choose among alternatives.

A sample case study is outlined in Appendix A. It deals with the problem of completing a program to print a calendar for a given year. The problem specifies that two subprograms are to be supplied: a function NumberOfDaysIn , which returns the number of days in a given month in a given year, and a procedure PrintMonth , which prints the calendar "page" for a given month. (The solution to this problem is treated in more detail in Clancy and Linn [4].)

Why aren't case studies more commonly used? The case study approach is atypical in introductory and intermediate programming courses. One reason is the relative scarcity of appropriate material. Published sources of case studies include the "Literate Programming" columns in Communications of the ACM [13], the books by Bentley [1][2], Kernighan and Plauger [6], and Clancy and Linn [4], and excerpts from books such as Ledgard and Tauer [9], Kruse [8], and Reges [12]. Much of this material is intended for experts or teachers, not students.

Many CS 1 and CS 2 courses are packed with details, and instructors may believe they have no room to include case studies. In such courses, however, it is easy for students to lose sight of the big picture, gain only superficial understanding of program design, and fail to appreciate the problem-solving power of a programming language.

Perhaps another reason that case studies aren't used is an impression by instructors that novice programmers are not ready, or inclined, to appreciate the issues discussed in a case study. Personal experience and informal surveys indicate, however, that students learn from case studies both in college introductory courses and in precollege programming classes.

Lastly, instructors may not be aware of the variety of ways to incorporate case studies into their classes? hence this paper. We describe how case studies can enhance laboratory and homework exercises, small group work, examinations, and lectures. We or all colleagues at Berkeley and elsewhere have tested, in introductory and intermediate programming courses, all the techniques we describe. These techniques are useful in more advanced settings as well.

Laboratory and homework assignments A case study presents opportunities for students to analyze, modify, or extend a large program, or reuse the code to solve a related problem. The accompanying narrative makes the code easier for the student to understand, and thus reduces the complexity and maximizes the benefit of the assigned exercises.

One set of exercises requires students to use a compiled executable version of the code. A version with the sizes of the data structures reduced for experiments is provided. With executable code? a source listing need not be provided? Students can still accumulate a substantial amount of information about a program, by predicting its behavior given sample input, providing input that produces a given bahavior, distinguishing legal from illegal input, and devising good examples to teach new users about the program. They can also probe the limits of the program: How much input can it handle? What are constraints on the input format? How are out-of-bounds or overflow cases handled? Finally, students can evaluate the user interface, and compare it and the program's capabilities to other similar programs they have used.

With online source code, students working together can play "debugging games". One partner (or staff member if students are to work individually) inserts a bug into the program; the other attempts to find it. This requires that students have read the program but do not have the code listing nearby. It encourages students to invent thorough sets of test data, and to think about what aspects of a program's style and organization facilitate testing and debugging.

Typical laboratory or homework assignments using online code include modifying a program to change its user interface, replacing its data structures, and adding or extending features. Such activities can be profitably done in teams as well as individually. They illustrate the importance of code readability, planning, and incremental development, and introduce subtle issues, for instance, that the program should be modified in the style in which it is written.

Other online exercises include using the code in a larger application, or solving a similar problem. The advantages of rewriting reusable code are apparent in both activities.

Students can also be encouraged to create their own case studies. This activity reveals student thinking and helps instructors make sense of students' understanding of the material. Instructors can also incorporate the student solutions into subsequent course activities.

One other activity, that of solving the problem before seeing the solution and accompanying discussion, would seem to be good preparation for a case study. Students might be expected to be more sensitive to the decisions described in the narrative. Linn and Clancy [10] noted, however, that this approach was not always productive. Some students, having written one program to solve the problem, weren't interested in alternative.

Example exercises involving the "Calendar" case study The solution programs in teh "Calendar" case study read a year from from the user, then print the calendar for that year. Students might be asked to perform experiments with executable versions of these programs, to determine how the programs handle years before the Gregorian reform of 1582, and whether erratic behavior results from negative or exceptionally large year values.

There are many places in the program to insert bugs, such as off-by-one initializations and off-by-one or reversed comparisons.

Possible modifications of the programs include highlighting of holidays or other significant days, and printing weekend days in a special format. Code from the calendar programs can be reused in a variety of applications involving date computations, and in programs to produce different kinds of calendars. Examples of the latter are the Jewish, Muslim, Mayan, Chinese, and French Revolutionary calendars, and a fantasy calendar without Mondays.

Small-group discussion Discussion is most effective when students can contribute diverse perspectives and expertise. Naturally-occurring differences in problem-solving style among group members provide good grist for discussion and brainstorming. For instance, what aspects of the style and organization of the program make it easy or hard to understand, and why? What parts of the program match code that group members have seen before? Which of the design or development decisions would group members have made differently, and why?

One way to ensure that students have different types of expertise is to ask subgroups to study different case studies and then present the ideas to the rest of the class.

Discussion can also take advantage of previous online exercises. What were various ways of approaching these exercises? What aspects of the style and organization of the program made it easy or hard to modify? How does one set of test results provide better evidence for the correctness of the program than another? How did a partnership divide the problem, and how were the skills of the partners put to effective use?

Finally, discussion can provide opportunities for students to reflect on their own behaviors. The discussion leader might ask the group to compare their abilities to detect errors in output, to locate errors in code once they�ve been detected, and to find the simplest ways to fix the errors. Discussion might also encourage students to recognize and admit their programming weaknesses, such as propensities to "rush to the computer" or to test too much code at once.

How can these skills be assessed? In the typical programming course, the end product of a programming assignment is graded rather than the supposedly "disciplined approach to design, coding, and testing" that created it. An exam is constrained by limits on the time available to take it and the time available to grade it. Exam questions tend to focus on isolated facts rather than on complex problems.

Case studies allow assessment of analysis, design, and development in the context of a challenging problem. Here are some example question patterns. They must, of course, be asked in the context of a particular case study.

Such questions, based on the context provided by the case study, are much less open-ended and much easier to grade than entire programs. They also require much less reading during an examination, since case studies can be reviewed in advance, than do questions in which a context must be set up from scratch. They therefore allow good programmers with reading deficiencies a better opportunity to display their knowledge.

The narrative description of design and development decisions is an important component for assessment. Linn and Clancy [10] found that students who received expert commentary did significantly better on their tests than students who received documented code without commentary.

Examples of assessment using the "Calendar" case study The programs described in the "Calendar" case study, though each no more than two pages of code, provide surprising opportunities for questions about analysis, design, and development. Here are some examples.

Lectures One might guess that lecturing about a case study would be difficult; the narrative description contains much of what a lecturer might wish to say about the problem solution. How, then, can a lecturer provide an interesting, enthusiastic presentation for students?

One good approach is to give suggestions about how to read the narrative and program: how to find the important parts, how to take notes on the program, what experiments to try, and what collection of input data should be built to test the program. The lecturer can also point out what programming patterns and good habits are illustrated in the case study, and relate them to students� previous experiences in the class. In addition, the lecturer might enrich the problem solution by supplying missing information and structure for the students, and by adapting the material to their experience and background. Hints for the homework are an obvious source of material. Much of the published case study material discusses the design but not the development stage; the lecturer might present a model sequence for testing and debugging the program.

Another source of lecture material is extension of the ideas in the case study. A lecturer might discuss how segments of the program could be used for other problem solutions, or discuss problems that can be solved in ways illustrated in the case study. A lecturer might also talk about more general applications of case study activities. An example might be a technique like mutation testing, in which the goal is to build a test suite that catches standard types of errors intentionally introduced into the program (see Budd [3]).

Finally, the lecturer can personalize the presentation with his or her opinions about controversial aspects of the design or development. Discussion of the lecturer's personal experience with similar problems or approaches can fascinate a class.

Summary As textbooks for CS 1 and CS 2 get thicker and thicker, are your students memorizing more and understanding less? Case studies allow students, guided by an expert, to explore and experiment with design, analysis, and modification of programs they might not be able to create on their own. Students can solve what seem like "real" problems by altering expert solutions. Instructors can assign homework, lab activities, and discussion topics related to real problems, and easily prepare examinations that both educate students and provide significant information about student progress.

Appendix A Outline of decisions encountered in the "Calendar" case study

• The University Library

Case studies: Embedding IDL as part of a programme level approach

Case study of the BSc Computer Science (COMU01)

Overview and outcomes of the BSc Computer Science project to embed IDL.

The Library's support for Computer Science undergraduate students has traditionally been provided through induction sessions at the beginning of each academic year.

The aim of this project was to explore where information and digital literacy (IDL) support could be embedded within the curriculum to provide the right support, in the right format, at the most appropriate time.

The Library Student Associate and Liaison Librarian explored the IDL resources available, assessed the relevance of IDL resources to their own studies, and identified gaps in the IDL offer from the perspective of a Computer Science student.

Recommendations from the project:

Developments will be made to the Library's online resources to address the identified need for guidance on referencing code in assignments and finding and using images in website creation.

The Library will co-investigate with the department guidance on the ethical reuse and referencing of code with a view to update the Library's Referencing Guides.

The  Discovering images tutorial  has been identified as a resource which can be modified to include specific information about using images in creating websites.

The Library Student Associates identified the need to promote the Library's IDL offer and that awareness of the online tutorials could be raised with academic staff with a view to embedding these into Blackboard modules. Modules identified were:

COM1001, COM1008, COM3420 Images: Discover, understand, reference Creating and using images

COM2009 Questioning and evaluating information

• The relevant  referencing guides  should be embedded in all applicable modules and assessment.

The Library will continue to collaborate with the department to embed the IDL support that is available and to promote this to students.

Reflections

Sofia bodurova, library student associate.

From the very beginning until the end I have felt great being able to participate in the programme development, doing research on how things can be done. This participation guided me in many ways about all of the resources and support that are provided by the Library.

Many students from the University, including me, have had a hard time finding information about plagiarism. However, during that time the Library has supported us.

After this project I would definitely start looking a bit more into the Library, many topics not covered by lectures are provided as workshops by the Library. The key idea of ours was to identify the gaps in the department which could be filled by the Library.

There are two key concepts that are used but not explained in many modules; they are 'Images: Discover, understand, reference' and 'Preventing plagiarism and reference management'.

What can be better in our case is to reduce the time it takes for a student to find everything on their own and have information provided on Blackboard. This can also save time for many lecturers who have to deal with enquiries in these areas.

Emily Herron, Liaison Librarian

It has been interesting to discover more about the assignments that students are working on and to learn from Sofia how the Library's resources could provide support with these.

One of the main messages we heard from all the Library Student Associates was how hidden the Library's online tutorials are. They need to be embedded within Blackboard modules at the point of student need and made easier to find.

The PLA lead and the department have been very supportive of the work that Sofia and the Library have undertaken. The next steps will be to work on the outcomes identified within the project.

This will be done in collaboration with the department with a particular emphasis on the need for guidance on referencing code in assignments and finding and using images in website creation.

Related information

Ask a question.

Email: [email protected]

Phone: +44 114 222 7200

Sheffield is a research university with a global reputation for excellence. We're a member of the Russell Group: one of the 24 leading UK universities for research and teaching.

Global Dimensions of Intellectual Property Rights in Science and Technology (1993)

Chapter: 12 a case study on computer programs, 12 a case study on computer programs.

PAMELA SAMUELSON

HISTORICAL OVERVIEW

Phase 1: the 1950s and early 1960s.

When computer programs were first being developed, proprietary rights issues were not of much concern. Software was often developed in academic or other research settings. Much progress in the programming field occurred as a result of informal exchanges of software among academics and other researchers. In the course of such exchanges, a program developed by one person might be extended or improved by a number of colleagues who would send back (or on to others) their revised versions of the software. Computer manufacturers in this period often provided software to customers of their machines to make their major product (i.e., computers) more commercially attractive (which caused the software to be characterized as "bundled" with the hardware).

To the extent that computer programs were distributed in this period by firms for whom proprietary rights in software were important, programs tended to be developed and distributed through restrictive trade secret licensing agreements. In general, these were individually negotiated with customers. The licensing tradition of the early days of the software industry has framed some of the industry expectations about proprietary rights issues, with implications for issues still being litigated today.

In the mid-1960s, as programs began to become more diverse and complex, as more firms began to invest in the development of programs, and as

some began to envision a wider market for software products, a public dialogue began to develop about what kinds of proprietary rights were or should be available for computer programs. The industry had trade secrecy and licensing protection, but some thought more legal protection might be needed.

Phase 2: Mid-1960s and 1970s

Copyright law was one existing intellectual property system into which some in the mid-1960s thought computer programs might potentially fit. Copyright had a number of potential advantages for software: it could provide a relatively long term of protection against unauthorized copying based on a minimal showing of creativity and a simple, inexpensive registration process. 1 Copyright would protect the work's ''expression," but not the "ideas" it contained. Others would be free to use the same ideas in other software, or to develop independently the same or a similar work. All that would be forbidden was the copying of expression from the first author's work.

In 1964, the U.S. Copyright Office considered whether to begin accepting registration of computer programs as copyrightable writings. It decided to do so, but only under its "rule of doubt" and then only on condition that a full text of the program be deposited with the office, which would be available for public review. 2

The Copyright Office's doubt about the copyrightability of programs

arose from a 1908 Supreme Court decision that had held that a piano roll was not an infringing "copy" of copyrighted music, but rather part of a mechanical device. 3 Mechanical devices (and processes) have traditionally been excluded from the copyright domain. 4 Although the office was aware that in machine-readable form, computer programs had a mechanical character, they also had a textual character, which was why the Copyright Office decided to accept them for registration.

The requirement that the full text of the source code of a program be deposited in order for a copyright in the program to be registered was consistent with a long-standing practice of the Copyright Office, 5 as well as with what has long been perceived to be the constitutional purpose of copyright, namely, promoting the creation and dissemination of knowledge. 6

Relatively few programs, however, were registered with the Copyright Office under this policy during the 1960s and 1970s. 7 Several factors may have contributed to this. Some firms may have been deterred by the requirement that the full text of the source code be deposited with the office and made available for public inspection, because this would have dispelled its trade secret status. Some may have thought a registration certificate issued under the rule of doubt might not be worth much. However, the main reason for the low number of copyright registrations was probably that a mass market in software still lay in the future. Copyright is useful mainly to protect mass-marketed products, and trade secrecy is quite adequate for programs with a small number of distributed copies.

Shortly after the Copyright Office issued its policy on the registrability of computer programs, the U.S. Patent Office issued a policy statement concerning its views on the patentability of computer programs. It rejected the idea that computer programs, or the intellectual processes that might be embodied in them, were patentable subject matter. 8 Only if a program was

claimed as part of a traditionally patentable industrial process (i.e., those involving the transformation of matter from one physical state to another) did the Patent Office intend to issue patents for program-related innovations. 9

Patents are typically available for inventive advances in machine designs or other technological products or processes on completion of a rigorous examination procedure conducted by a government agency, based on a detailed specification of what the claimed invention is, how it differs from the prior art, and how the invention can be made. Although patent rights are considerably shorter in duration than copyrights, patent rights are considered stronger because no one may make, use, or sell the claimed invention without the patent owner's permission during the life of the patent. (Patents give rights not just against someone who copies the protected innovation, but even against those who develop it independently.) Also, much of what copyright law would consider to be unprotectable functional content ("ideas") if described in a book can be protected by patent law.

The Patent Office's policy denying the patentability of program innovations was consistent with the recommendations of a presidential commission convened to make suggestions about how the office could more effectively cope with an "age of exploding technology." The commission also recommended that patent protection not be available for computer program innovations. 10

Although there were some appellate decisions in the late 1960s and

early 1970s overturning Patent Office rejections of computer program-related applications, few software developers looked to the patent system for protection after two U.S. Supreme Court decisions in the 1970s ruled that patent protection was not available for algorithms. 11 These decisions were generally regarded as calling into question the patentability of all software innovations, although some continued to pursue patents for their software innovations notwithstanding these decisions. 12

As the 1970s drew to a close, despite the seeming availability of copyright protection for computer programs, the software industry was still relying principally on trade secrecy and licensing agreements. Patents seemed largely, if not totally, unavailable for program innovations. Occasional suggestions were made that a new form of legal protection for computer programs should be devised, but the practice of the day was trade secrecy and licensing, and the discourse about additional protection was focused overwhelmingly on copyright.

During the 1960s and 1970s the computer science research community grew substantially in size. Although more software was being distributed under restrictive licensing agreements, much software, as well as innovative ideas about how to develop software, continued to be exchanged among researchers in this field. The results of much of this research were published and discussed openly at research conferences. Toward the end of this period, a number of important research ideas began to make their way into commercial projects, but this was not seen as an impediment to research by computer scientists because the commercial ventures tended to arise after the research had been published. Researchers during this period did not, for the most part, seek proprietary rights in their software or software ideas, although other rewards (such as tenure or recognition in the field) were available to those whose innovative research was published.

Phase 3: The 1980s

Four significant developments in the 1980s changed the landscape of the software industry and the intellectual property rights concerns of those who developed software. Two were developments in the computing field; two were legal developments.

The first significant computing development was the introduction to the market of the personal computer (PC), a machine made possible by improvements in the design of semiconductor chips, both as memory storage

devices and as processing units. A second was the visible commercial success of some early PC applications software—most notably, Visicalc, and then Lotus 1-2-3—which significantly contributed to the demand for PCs as well as making other software developers aware that fortunes could be made by selling software. With these developments, the base for a large mass market in software was finally in place.

During this period, computer manufacturers began to realize that it was to their advantage to encourage others to develop application programs that could be executed on their brand of computers. One form of encouragement involved making available to software developers whatever interface information would be necessary for development of application programs that could interact with the operating system software provided with the vendor's computers (information that might otherwise have been maintained as a trade secret). Another form of encouragement was pioneered by Apple Computer, which recognized the potential value to consumers (and ultimately to Apple) of having a relatively consistent "look and feel" to the applications programs developed to run on Apple computers. Apple developed detailed guidelines for applications developers to aid in the construction of this consistent look and feel.

The first important legal development—one which was in place when the first successful mass-marketed software applications were introduced into the market—was passage of amendments to the copyright statute in 1980 to resolve the lingering doubt about whether copyright protection was available for computer programs. 13 These amendments were adopted on the recommendation of the National Commission on New Technological Uses of Copyrighted Works (CONTU), which Congress had established to study a number of "new technology" issues affecting copyrighted works. The CONTU report emphasized the written nature of program texts, which made them seem so much like written texts that had long been protected by copyright law. The CONTU report noted the successful expansion of the boundaries of copyright over the years to take in other new technology products, such as photographs, motion pictures, and sound recordings. It predicted that computer programs could also be accommodated in the copyright regime. 14

Copyright law was perceived by CONTU as the best alternative for protection of computer programs under existing intellectual property regimes. Trade secrecy, CONTU noted, was inherently unsuited for mass-marketed products because the first sale of the product on the open market would dispel the secret. CONTU observed that Supreme Court rulings had cast

doubts on the availability of patent protection for software. CONTU's confidence in copyright protection for computer programs was also partly based on an economic study it had commissioned. This economic study regarded copyright as suitable for protecting software against unauthorized copying after sale of the first copy of it in the marketplace, while fostering the development of independently created programs. The CONTU majority expressed confidence that judges would be able to draw lines between protected expression and unprotected ideas embodied in computer programs, just as they did routinely with other kinds of copyrighted works.

A strong dissenting view was expressed by the novelist John Hersey, one of the members of the CONTU commission, who regarded programs as too mechanical to be protected by copyright law. Hersey warned that the software industry had no intention to cease the use of trade secrecy for software. Dual assertion of trade secrecy and copyright seemed to him incompatible with copyright's historical function of promoting the dissemination of knowledge.

Another development during this period was that the Copyright Office dropped its earlier requirement that the full text of source code be deposited with it. Now only the first and last 25 pages of source code had to be deposited to register a program. The office also decided it had no objection if the copyright owner blacked out some portions of the deposited source code so as not to reveal trade secrets. This new policy was said to be consistent with the new copyright statute that protected both published and unpublished works alike, in contrast to the prior statutes that had protected mainly published works. 15

With the enactment of the software copyright amendments, software developers had a legal remedy in the event that someone began to mass-market exact or near-exact copies of the developers' programs in competition with the owner of the copyright in the program. Unsurprisingly, the first software copyright cases involved exact copying of the whole or substantial portions of program code, and in them, the courts found copyright infringement. Copyright litigation in the mid- and late 1980s began to grapple with questions about what, besides program code, copyright protects about computer programs. Because the "second-generation" litigation affects the current legal framework for the protection of computer programs, the issues raised by these cases will be dealt with in the next section.

As CONTU Commissioner Hersey anticipated, software developers did not give up their claims to the valuable trade secrets embodied in their programs after enactment of the 1980 amendments to the copyright statute.

To protect those secrets, developers began distributing their products in machine-readable form, often relying on "shrink-wrap" licensing agreements to limit consumer rights in the software. 16 Serious questions exist about the enforceability of shrink-wrap licenses, some because of their dubious contractual character 17 and some because of provisions that aim to deprive consumers of rights conferred by the copyright statute. 18 That has not led, however, to their disuse.

One common trade secret-related provision of shrink-wrap licenses, as well as of many negotiated licenses, is a prohibition against decompilation or disassembly of the program code. Such provisions are relied on as the basis of software developer assertions that notwithstanding the mass distribution of a program, the program should be treated as unpublished copyrighted works as to which virtually no fair use defenses can be raised. 19

Those who seek to prevent decompilation of programs tend to assert that since decompilation involves making an unauthorized copy of the program, it constitutes an improper means of obtaining trade secrets in the program. Under this theory, decompilation of program code results in three unlawful acts: copyright infringement (because of the unauthorized copy made during the decompilation process), trade secret misappropriation (because the secret has been obtained by improper means, i.e., by copyright

infringement), and a breach of the licensing agreement (which prohibits decompilation).

Under this theory, copyright law would become the legal instrument by which trade secrecy could be maintained in a mass-marketed product, rather than a law that promotes the dissemination of knowledge. Others regard decompilation as a fair use of a mass-marketed program and, shrink-wrap restrictions to the contrary, as unenforceable. This issue has been litigated in the United States, but has not yet been resolved definitively. 20 The issue remains controversial both within the United States and abroad.

A second important legal development in the early 1980s—although one that took some time to become apparent—was a substantial shift in the U.S. Patent and Trademark Office (PTO) policy concerning the patentability of computer program-related inventions. This change occurred after the 1981 decision by the U.S. Supreme Court in Diamond v. Diehr, which ruled that a rubber curing process, one element of which was a computer program, was a patentable process. On its face, the Diehr decision seemed consistent with the 1966 Patent Office policy and seemed, therefore, not likely to lead to a significant change in patent policy regarding software innovations. 21 By the mid-1980s, however, the PTO had come to construe the Court's ruling broadly and started issuing a wide variety of computer program-related patents. Only "mathematical algorithms in the abstract" were now thought unpatentable. Word of the PTO's new receptivity to software patent applications spread within the patent bar and gradually to software developers.

During the early and mid-1980s, both the computer science field and the software industry grew very significantly. Innovative ideas in computer science and related research fields were widely published and disseminated. Software was still exchanged by researchers, but a new sensitivity to intellectual property rights began to arise, with general recognition that unauthorized copying of software might infringe copyrights, especially if done with a commercial purpose. This was not perceived as presenting a serious obstacle to research, for it was generally understood that a reimplementation of the program (writing one's own code) would be

noninfringing. 22 Also, much of the software (and ideas about software) exchanged by researchers during the early and mid-1980s occurred outside the commercial marketplace. Increasingly, the exchanges took place with the aid of government-subsidized networks of computers.

Software firms often benefited from the plentiful availability of research about software, as well as from the availability of highly trained researchers who could be recruited as employees. Software developers began investing more heavily in research and development work. Some of the results of this research was published and/or exchanged at technical conferences, but much was kept as a trade secret and incorporated in new products.

By the late 1980s, concerns began arising in the computer science and related fields, as well as in the software industry and the legal community, about the degree of intellectual property protection needed to promote a continuation of the high level of innovation in the software industry. 23 Although most software development firms, researchers, and manufacturers of computers designed to be compatible with the leading firms' machines seemed to think that copyright (complemented by trade secrecy) was adequate to their needs, the changing self-perception of several major computer manufacturers led them to push for more and "stronger" protection. (This concern has been shared by some successful software firms whose most popular programs were being "cloned" by competitors.) Having come to realize that software was where the principal money of the future would be made, these computer firms began reconceiving themselves as software developers. As they did so, their perspective on software protection issues changed as well. If they were going to invest in software development, they wanted "strong'' protection for it. They have, as a consequence, become among the most vocal advocates of strong copyright, as well as of patent protection for computer programs. 24

CURRENT LEGAL APPROACHES IN THE UNITED STATES

Software developers in the United States are currently protecting software products through one or more of the following legal protection mechanisms: copyright, trade secret, and/or patent law. Licensing agreements often supplement these forms of protection. Some software licensing agreements are negotiated with individual customers; others are printed forms found under the plastic shrink-wrap of a mass-marketed package. 25 Few developers rely on only one form of legal protection. Developers seem to differ somewhat on the mix of legal protection mechanisms they employ as well as on the degree of protection they expect from each legal device.

Although the availability of intellectual property protection has unquestionably contributed to the growth and prosperity of the U.S. software industry, some in the industry and in the research community are concerned that innovation and competition in this industry will be impeded rather than enhanced if existing intellectual property rights are construed very broadly. 26 Others, however, worry that courts may not construe intellectual property rights broadly enough to protect what is most valuable about software, and if too little protection is available, there may be insufficient incentives to invest in software development; hence innovation and competition may be retarded through underprotection. 27 Still others (mainly lawyers) are confident that the software industry will continue to prosper and grow under the existing intellectual property regimes as the courts "fill out" the details of software protection on a case-by-case basis as they have been doing for the past several years. 28

What's Not Controversial

Although the main purpose of the discussion of current approaches is to give an overview of the principal intellectual property issues about which there is controversy in the technical and legal communities, it may be wise to begin with a recognition of a number of intellectual property issues as to which there is today no significant controversy. Describing only the aspects of the legal environment as to which controversies exist would risk creating a misimpression about the satisfaction many software developers and lawyers have with some aspects of intellectual property rights they now use to protect their and their clients' products.

One uncontroversial aspect of the current legal environment is the use of copyright to protect against exact or near-exact copying of program code. Another is the use of copyright to protect certain aspects of user interfaces, such as videogame graphics, that are easily identifiable as "expressive" in a traditional copyright sense. Also relatively uncontroversial is the use of copyright protection for low-level structural details of programs, such as the instruction-by-instruction sequence of the code. 29

The use of trade secret protection for the source code of programs and other internally held documents concerning program design and the like is similarly uncontroversial. So too is the use of licensing agreements negotiated with individual customers under which trade secret software is made available to licensees when the number of licensees is relatively small and when there is a reasonable prospect of ensuring that licensees will take adequate measures to protect the secrecy of the software. Patent protection for industrial processes that have computer program elements, such as the rubber curing process in the Diehr case, is also uncontroversial.

Substantial controversies exist, however, about the application of copyright law to protect other aspects of software, about patent protection for other kinds of software innovations, about the enforceability of shrink-wrap licensing agreements, and about the manner in which the various forms of legal protection seemingly available to software developers interrelate in the protection of program elements (e.g., the extent to which copyright and trade secret protection can coexist in mass-marketed software).

Controversies Arising From Whelan v. Jaslow

Because quite a number of the most contentious copyright issues arise from the Whelan v. Jaslow decision, this subsection focuses on that case. In the summer of 1986, the Third Circuit Court of Appeals affirmed a trial court decision in favor of Whelan Associates in its software copyright lawsuit against Jaslow Dental Laboratories. 30 Jaslow's program for managing dental lab business functions used some of the same data and file structures as Whelan's program (to which Jaslow had access), and five subroutines of Jaslow's program functioned very similarly to Whelan's. The trial court inferred that there were substantial similarities in the underlying structure of the two programs based largely on a comparison of similarities in the user interfaces of the two programs, even though user interface similarities were not the basis for the infringement claim. Jaslow's principal defense was that Whelan's copyright protected only against exact copying of program code, and since there were no literal similarities between the programs, no copyright infringement had occurred.

In its opinion on this appeal, the Third Circuit stated that copyright protection was available for the "structure, sequence, and organization" (sso) of a program, not just the program code. (The court did not distinguish between high- and low-level structural features of a program.) The court analogized copyright protection for program sso to the copyright protection available for such things as detailed plot sequences in novels. The court also emphasized that the coding of a program was a minor part of the cost of development of a program. The court expressed fear that if copyright protection was not accorded to sso, there would be insufficient incentives to invest in the development of software.

The Third Circuit's Whelan decision also quoted with approval from that part of the trial court opinion stating that similarities in the manner in which programs functioned could serve as a basis for a finding of copyright infringement. Although recognizing that user interface similarities did not necessarily mean that two programs had similar underlying structures (thereby correcting an error the trial judge had made), the appellate court thought that user interface similarities might still be some evidence of underlying structural similarities. In conjunction with other evidence in the case, the Third Circuit decided that infringement had properly been found.

Although a number of controversies have arisen out of the Whelan opinion, the aspect of the opinion that has received the greatest attention is the test the court used for determining copyright infringement in computer

program cases. The " Whelan test" regards the general purpose or function of a program as its unprotectable "idea." All else about the program is, under the Whelan test, protectable "expression'' unless there is only one or a very small number of ways to achieve the function (in which case idea and expression are said to be "merged," and what would otherwise be expression is treated as an idea). The sole defense this test contemplates for one who has copied anything more detailed than the general function of another program is that copying that detail was "necessary" to perform that program function. If there is in the marketplace another program that does the function differently, courts applying the Whelan test have generally been persuaded that the copying was unjustified and that what was taken must have been "expressive."

Although the Whelan test has been used in a number of subsequent cases, including the well-publicized Lotus v. Paperback case, 31 some judges have rejected it as inconsistent with copyright law and tradition, or have found ways to distinguish the Whelan case when employing its test would have resulted in a finding of infringement. 32

Many commentators assert that the Whelan test interprets copyright

protection too expansively. 33 Although the court in Whelan did not seem to realize it, the Whelan test would give much broader copyright protection to computer programs than has traditionally been given to novels and plays, which are among the artistic and fanciful works generally accorded a broader scope of protection than functional kinds of writings (of which programs would seem to be an example). 34 The Whelan test would forbid reuse of many things people in the field tend to regard as ideas. 35 Some commentators have suggested that because innovation in software tends to be of a more incremental character than in some other fields, and especially given the long duration of copyright protection, the Whelan interpretation of the scope of copyright is likely to substantially overprotect software. 36

One lawyer-economist, Professor Peter Menell, has observed that the model of innovation used by the economists who did the study of software for CONTU is now considered to be an outmoded approach. 37 Those econo-

mists focused on a model that considered what incentives would be needed for development of individual programs in isolation. Today, economists would consider what protection would be needed to foster innovation of a more cumulative and incremental kind, such as has largely typified the software field. In addition, the economists on whose work CONTU relied did not anticipate the networking potential of software and consequently did not study what provisions the law should make in response to this phenomenon. Menell has suggested that with the aid of their now more refined model of innovation, economists today might make somewhat different recommendations on software protection than they did in the late 1970s for CONTU. 38

As a matter of copyright law, the principal problem with the Whelan test is its incompatibility with the copyright statute, the case law properly interpreting it, and traditional principles of copyright law. The copyright statute provides that not only ideas, but also processes, procedures, systems, and methods of operation, are unprotectable elements of copyrighted works. 39 This provision codifies some long-standing principles derived from U.S. copyright case law, such as the Supreme Court's century-old Baker v. Selden decision that ruled that a second author did not infringe a first author's copyright when he put into his own book substantially similar ledger sheets to those in the first author's book. The reason the Court gave for its ruling was that Selden's copyright did not give him exclusive rights to the bookkeeping system, but only to his explanation or description of it. 40 The ordering and arrangement of columns and headings on the ledger sheets were part of the system; to get exclusive rights in this, the Court said that Selden would have to get a patent.

The statutory exclusion from copyright protection for methods, processes, and the like was added to the copyright statute in part to ensure that the scope of copyright in computer programs would not be construed too broadly. Yet, in cases in which the Whelan test has been employed, the courts have tended to find the presence of protectable "expression" when they perceive there to be more than a couple of ways to perform some function, seeming not to realize that there may be more than one "method" or "system" or "process" for doing something, none of which is properly protected by copyright law. The Whelan test does not attempt to exclude

methods or processes from the scope of copyright protection, and its recognition of functionality as a limitation on the scope of copyright is triggered only when there are no alternative ways to perform program functions.

Whelan has been invoked by plaintiffs not only in cases involving similarities in the internal structural design features of programs, but also in many other kinds of cases. sso can be construed to include internal interface specifications of a program, the layout of elements in a user interface, and the sequence of screen displays when program functions are executed, among other things. Even the manner in which a program functions can be said to be protectable by copyright law under Whelan . The case law on these issues and other software issues is in conflict, and resolution of these controversies cannot be expected very soon.

Traditionalist Versus Strong Protectionist View of What Copyright Law Does and Does Not Protect in Computer Programs

Traditional principles of copyright law, when applied to computer programs, would tend to yield only a "thin" scope of protection for them. Unquestionably, copyright protection would exist for the code of the program and the kinds of expressive displays generated when program instructions are executed, such as explanatory text and fanciful graphics, which are readily perceptible as traditional subject matters of copyright law. A traditionalist would regard copyright protection as not extending to functional elements of a program, whether at a high or low level of abstraction, or to the functional behavior that programs exhibit. Nor would copyright protection be available for the applied know-how embodied in programs, including program logic. 41 Copyright protection would also not be available for algorithms or other structural abstractions in software that are constituent elements of a process, method, or system embodied in a program.

Efficient ways of implementing a function would also not be protectable by copyright law under the traditionalist view, nor would aspects of software design that make the software easier to use (because this bears on program functionality). The traditionalist would also not regard making a limited number of copies of a program to study it and extract interface information or other ideas from the program as infringing conduct, because computer programs are a kind of work for which it is necessary to make a copy to "read" the text of the work. 42 Developing a program that incorporates interface information derived from decompilation would also, in the traditionalist view, be noninfringing conduct.

If decompilation and the use of interface information derived from the study of decompiled code were to be infringing acts, the traditionalist would regard copyright as having been turned inside out, for instead of promoting the dissemination of knowledge as has been its traditional purpose, copyright law would become the principal means by which trade secrets would be maintained in widely distributed copyrighted works. Instead of protecting only expressive elements of programs, copyright would become like a patent: a means by which to get exclusive rights to the configuration of a machine—without meeting stringent patent standards or following the strict procedures required to obtain patent protection. This too would seem to turn copyright inside out.

Because interfaces, algorithms, logic, and functionalities of programs are aspects of programs that make them valuable, it is understandable that some of those who seek to maximize their financial returns on software investments have argued that "strong" copyright protection is or should be available for all valuable features of programs, either as part of program sso or under the Whelan "there's-another-way-to-do-it" test. 43 Congress seems to have intended for copyright law to be interpreted as to programs on a case-by-case basis, and if courts determine that valuable features should be considered "expressive," the strong protectionists would applaud this common law evolution. If traditional concepts of copyright law and its purposes do not provide an adequate degree of protection for software innovation, they see it as natural that copyright should grow to provide it. Strong protectionists tend to regard traditionalists as sentimental Luddites who do not appreciate that what matters is for software to get the degree of protection it needs from the law so that the industry will thrive.

Although some cases, most notably the Whelan and Lotus decisions, have adopted the strong protectionist view, traditionalists will tend to regard these decisions as flawed and unlikely to be affirmed in the long run because they are inconsistent with the expressed legislative intent to have traditional principles of copyright law applied to software. Some copyright traditionalists favor patent protection for software innovations on the ground that the valuable functional elements of programs do need protection to create proper incentives for investing in software innovations, but that this protection should come from patent law, not from copyright law.

Controversy Over "Software Patents"

Although some perceive patents as a way to protect valuable aspects of programs that cannot be protected by copyright law, those who argue for patents for software innovations do not rely on the "gap-filling" concern alone. As a legal matter, proponents of software patents point out that the patent statute makes new, nonobvious, and useful "processes" patentable. Programs themselves are processes; they also embody processes. 44 Computer hardware is clearly patentable, and it is a commonplace in the computing field that any tasks for which a program can be written can also be implemented in hardware. This too would seem to support the patentability of software.

Proponents also argue that protecting program innovations by patent law is consistent with the constitutional purpose of patent law, which is to promote progress in the "useful arts." Computer program innovations are technological in nature, which is said to make them part of the useful arts to which the Constitution refers. Proponents insist that patent law has the same potential for promoting progress in the software field as it has had for promoting progress in other technological fields. They regard attacks on patents for software innovations as reflective of the passing of the frontier in the software industry, a painful transition period for some, but one necessary if the industry is to have sufficient incentives to invest in software development.

Some within the software industry and the technical community, however, oppose patents for software innovations. 45 Opponents tend to make two kinds of arguments against software patents, often without distinguishing between them. One set of arguments questions the ability of the PTO to deal well with software patent applications. Another set raises more fundamental questions about software patents. Even assuming that the PTO could begin to do a good job at issuing software patents, some question whether

innovation in the software field will be properly promoted if patents become widely available for software innovations. The main points of both sets of arguments are developed below.

Much of the discussion in the technical community has focused on "bad" software patents that have been issued by the PTO. Some patents are considered bad because the innovation was, unbeknownst to the PTO, already in the state of the art prior to the date of invention claimed in the patent. Others are considered bad because critics assert that the innovations they embody are too obvious to be deserving of patent protection. Still others are said to be bad because they are tantamount to a claim for performing a particular function by computer or to a claim for a law of nature, neither of which is regarded as patentable subject matter. Complaints abound that the PTO, after decades of not keeping up with developments in this field, is so far out of touch with what has been and is happening in the field as to be unable to make appropriate judgments on novelty and nonobviousness issues. Other complaints relate to the office's inadequate classification scheme for software and lack of examiners with suitable education and experience in computer science and related fields to make appropriate judgments on software patent issues. 46

A somewhat different point is made by those who assert that the software industry has grown to its current size and prosperity without the aid of patents, which causes them to question the need for patents to promote innovation in this industry. 47 The highly exclusionary nature of patents (any use of the innovation without the patentee's permission is infringing) contrasts sharply with the tradition of independent reinvention in this field. The high expense associated with obtaining and enforcing patents raises concerns about the increased barriers to entry that may be created by the patenting of software innovations. Since much of the innovation in this industry has come from small firms, policies that inhibit entry by small firms may not promote innovation in this field in the long run. Similar questions arise as to whether patents will promote a proper degree of innovation in an incremental industry such as the software industry. It would be possible to undertake an economic study of conditions that have promoted and are promoting progress in the software industry to serve as a basis for a policy decision on software patents, but this has not been done to date.

Some computer scientists and mathematicians are also concerned about patents that have been issuing for algorithms, 48 which they regard as dis-

coveries of fundamental truths that should not be owned by anyone. Because any use of a patented algorithm within the scope of the claims—whether by an academic or a commercial programmer, whether one knew of the patent or not—may be an infringement, some worry that research on algorithms will be slowed down by the issuance of algorithm patents. One mathematical society has recently issued a report opposing the patenting of algorithms. 49 Others, including Richard Stallman, have formed a League for Programming Freedom.

There is substantial case law to support the software patent opponent position, notwithstanding the PTO change in policy. 50 Three U.S. Supreme Court decisions have stated that computer program algorithms are unpatentable subject matter. Other case law affirms the unpatentability of processes that involve the manipulation of information rather than the transformation of matter from one physical state to another.

One other concern worth mentioning if both patents and copyrights are used to protect computer program innovations is whether a meaningful boundary line can be drawn between the patent and copyright domains as regards software. 51 A joint report of the U.S. PTO and the Copyright Office optimistically concludes that no significant problems will arise from the coexistence of these two forms of protection for software because copyright law will only protect program "expression" whereas patent law will only protect program "processes." 52

Notwithstanding this report, I continue to be concerned with the patent/ copyright interface because of the expansive interpretations some cases, particularly Whelan, have given to the scope of copyright protection for programs. This prefigures a significant overlap of copyright and patent law as to software innovations. This overlap would undermine important economic and public policy goals of the patent system, which generally leaves in the public domain those innovations not novel or nonobvious enough to be patented. Mere "originality" in a copyright sense is not enough to make an innovation in the useful arts protectable under U.S. law. 53

A concrete example may help illustrate this concern. Some patent lawyers report getting patents on data structures for computer programs.

The Whelan decision relied in part on similarities in data structures to prove copyright infringement. Are data structures "expressive" or "useful"? When one wants to protect a data structure of a program by copyright, does one merely call it part of the sso of the program, whereas if one wants to patent it, one calls it a method (i.e., a process) of organizing data for accomplishing certain results? What if anything does copyright's exclusion from protection of processes embodied in copyrighted works mean as applied to data structures? No clear answer to these questions emerges from the case law.

Nature of Computer Programs and Exploration of a Modified Copyright Approach

It may be that the deeper problem is that computer programs, by their very nature, challenge or contradict some fundamental assumptions of the existing intellectual property regimes. Underlying the existing regimes of copyright and patent law are some deeply embedded assumptions about the very different nature of two kinds of innovations that are thought to need very different kinds of protection owing to some important differences in the economic consequences of their protection. 54

In the United States, these assumptions derive largely from the U.S. Constitution, which specifically empowers Congress "to promote the progress of science [i.e., knowledge] and useful arts [i.e., technology], by securing for limited times to authors and inventors the exclusive right to their respective writings and discoveries." 55 This clause has historically been parsed as two separate clauses packaged together for convenience: one giving Congress power to enact laws aimed at promoting the progress of knowledge by giving authors exclusive rights in their writings, and the other giving Congress power to promote technological progress by giving inventors exclusive rights in their technological discoveries. Copyright law implements the first power, and patent law the second.

Owing partly to the distinctions between writings and machines, which the constitutional clause itself set up, copyright law has excluded machines

and other technological subject matters from its domain. 56 Even when described in a copyrighted book, an innovation in the useful arts was considered beyond the scope of copyright protection. The Supreme Court's Baker v. Selden decision reflects this view of the constitutional allocation. Similarly, patent law has historically excluded printed matter (i.e., the contents of writings) from its domain, notwithstanding the fact that printed matter may be a product of a manufacturing process. 57 Also excluded from the patent domain have been methods of organizing, displaying, and manipulating information (i.e., processes that might be embodied in writings, for example mathematical formulas), notwithstanding the fact that "processes" are named in the statute as patentable subject matter. They were not, however, perceived to be "in the useful arts" within the meaning of the constitutional clause.

The constitutional clause has been understood as both a grant of power and a limitation on power. Congress cannot, for example, grant perpetual patent rights to inventors, for that would violate the "limited times" provision of the Constitution. Courts have also sometimes ruled that Congress cannot, under this clause, grant exclusive rights to anyone but authors and inventors. In the late nineteenth century, the Supreme Court struck down the first federal trademark statute on the ground that Congress did not have power to grant rights under this clause to owners of trademarks who were neither "authors" nor "inventors." 58 A similar view was expressed in last year's Feist Publications v. Rural Telephone Services decision by the Supreme Court, which repeatedly stated that Congress could not constitutionally protect the white pages of telephone books through copyright law because to be an "author" within the meaning of the Constitution required some creativity in expression that white pages lacked. 59

Still other Supreme Court decisions have suggested that Congress could not constitutionally grant exclusive rights to innovators in the useful arts who were not true "inventors." 60 Certain economic assumptions are connected with this view, including the assumption that more modest innovations in the useful arts (the work of a mere mechanic) will be forthcoming without the grant of the exclusive rights of a patent, but that the incentives of patent rights are necessary to make people invest in making significant technological advances and share the results of their work with the public instead of keeping them secret.

One reason the United States does not have a copyright-like form of protection for industrial designs, as do many other countries, is because of lingering questions about the constitutionality of such legislation. In addition, concerns exist that the economic consequences of protecting uninventive technological advances will be harmful. So powerful are the prevailing patent and copyright paradigms that when Congress was in the process of considering the adoption of a copyright-like form of intellectual property protection for semiconductor chip designs, there was considerable debate about whether Congress had constitutional power to enact such a law. It finally decided it did have such power under the commerce clause, but even then was not certain.

As this discussion reveals, the U.S. intellectual property law has long assumed that something is either a writing (in which case it is protectable, if at all, by copyright law) or a machine (in which case it is protectable, if at all, by patent law), but cannot be both at the same time. However, as Professor Randall Davis has so concisely said, software is "a machine whose medium of construction happens to be text." 61 Davis regards the act of creating computer programs as inevitably one of both authorship and invention. There may be little or nothing about a computer program that is not, at base, functional in nature, and nothing about it that does not have roots in the text. Because of this, it will inevitably be difficult to draw meaningful boundaries for patents and copyrights as applied to computer programs.

Another aspect of computer programs that challenges the assumptions of existing intellectual property systems is reflected in another of Professor Davis's observations, namely, that "programs are not only texts; they also behave." 62 Much of the dynamic behavior of computer programs is highly functional in nature. If one followed traditional copyright principles, this functional behavior—no matter how valuable it might be—would be considered outside the scope of copyright law. 63 Although the functionality of program behavior might seem at first glance to mean that patent protection would be the obvious form of legal protection for it, as a practical matter, drafting patent claims that would adequately capture program behavior as an invention is infeasible. There are at least two reasons for this: it is partly because programs are able to exhibit such a large number and variety of states that claims could not reasonably cover them, and partly because of

the ''gestalt"-like character of program behavior, something that makes a more copyright-like approach desirable.

Some legal scholars have argued that because of their hybrid character as both writings and machines, computer programs need a somewhat different legal treatment than either traditional patent or copyright law would provide. 64 They have warned of distortions in the existing legal systems likely to occur if one attempts to integrate such a hybrid into the traditional systems as if it were no different from the traditional subject matters of these systems. 65 Even if the copyright and patent laws could be made to perform their tasks with greater predictability than is currently the case, these authors warn that such regimes may not provide the kind of protection that software innovators really need, for most computer programs will be legally obvious for patent purposes, and programs are, over time, likely to be assimilated within copyright in a manner similar to that given to "factual" and "functional" literary works that have only "thin" protection against piracy. 66

Professor Reichman has reported on the recurrent oscillations between states of under- and overprotection when legal systems have tried to cope with another kind of legal hybrid, namely, industrial designs (sometimes referred to as "industrial art"). Much the same pattern seems to be emerging in regard to computer programs, which are, in effect, "industrial literature." 67

The larger problems these hybrids present is that of protecting valuable forms of applied know-how embodied in incremental innovation that cannot successfully be maintained as trade secrets:

[M]uch of today's most advanced technology enjoys a less favorable competitive position than that of conventional machinery because the unpatentable, intangible know-how responsible for its commercial value becomes embodied in products that are distributed on the open market. A product of the new technologies, such as a computer program, an integrated circuit

design, or even a biogenetically altered organism may thus bear its know-how on its face, a condition that renders it as vulnerable to rapid appropriation by second-comers as any published literary or artistic work.

From this perspective, a major problem with the kinds of innovative know-how underlying important new technologies is that they do not lend themselves to secrecy even when they represent the fruit of enormous investment in research and development. Because third parties can rapidly duplicate the embodied information and offer virtually the same products at lower prices than those of the originators, there is no secure interval of lead time in which to recuperate the originators' initial investment or their losses from unsuccessful essays, not to mention the goal of turning a profit. 68

From a behavioral standpoint, investors in applied scientific know-how find the copyright paradigm attractive because of its inherent disposition to supply artificial lead time to all comers without regard to innovative merit and without requiring originators to preselect the products that are most worthy of protection. 69

Full copyright protection, however, with its broad notion of equivalents geared to derivative expressions of an author's personality is likely to disrupt the workings of the competitive market for industrial products. For this and other reasons, Professor Reichman argues that a modified copyright approach to the protection of computer programs (and other legal hybrids) would be a preferable framework for protecting the applied know-how they embody than either the patent or the copyright regime would presently provide. Similar arguments can be made for a modified form of copyright protection for the dynamic behavior of programs. A modified copyright approach might involve a short duration of protection for original valuable functional components of programs. It could be framed to supplement full copyright protection for program code and traditionally expressive elements of text and graphics displayed when programs execute, features of software that do not present the same dangers of competitive disruption from full copyright protection.

The United States is, in large measure, already undergoing the development of a sui generis law for protection of computer software through case-by-case decisions in copyright lawsuits. Devising a modified copyright approach to protecting certain valuable components that are not suitably protected under the current copyright regime would have the advantage of allowing a conception of the software protection problem as a whole, rather than on a piecemeal basis as occurs in case-by-case litigation in which the

skills of certain attorneys and certain facts may end up causing the law to develop in a skewed manner. 70

There are, however, a number of reasons said to weigh against sui generis legislation for software, among them the international consensus that has developed on the use of copyright law to protect software and the trend toward broader use of patents for software innovations. Some also question whether Congress would be able to devise a more appropriate sui generis system for protecting software than that currently provided by copyright. Some are also opposed to sui generis legislation for new technology products such as semiconductor chips and software on the ground that new intellectual property regimes will make intellectual property law more complicated, confusing, and uncertain.

Although there are many today who ardently oppose sui generis legislation for computer programs, these same people may well become among the most ardent proponents of such legislation if the U.S. Supreme Court, for example, construes the scope of copyright protection for programs to be quite thin, and reiterates its rulings in Benson, Flook, and Diehr that patent protection is unavailable for algorithms and other information processes embodied in software.

INTERNATIONAL PERSPECTIVES

After adopting copyright as a form of legal protection for computer programs, the United States campaigned vigorously around the world to persuade other nations to protect computer programs by copyright law as well. These efforts have been largely successful. Although copyright is now an international norm for the protection of computer software, the fine details of what copyright protection for software means, apart from protection against exact copying of program code, remain somewhat unclear in other nations, just as in the United States.

Other industrialized nations have also tended to follow the U.S. lead concerning the protection of computer program-related inventions by patent

law. 71 Some countries that in the early 1960s were receptive to the patenting of software innovations became less receptive after the Gottschalk v. Benson decision by the U.S. Supreme Court. Some even adopted legislation excluding computer programs from patent protection. More recently, these countries are beginning to issue more program-related patents, once again paralleling U.S. experience, although as in the United States, the standards for patentability of program-related inventions are somewhat unclear. 72 If the United States and Japan continue to issue a large number of computer program-related patents, it seems quite likely other nations will follow suit.

There has been strong pressure in recent years to include relatively specific provisions about intellectual property issues (including those affecting computer programs) as part of the international trade issues within the framework of the General Agreement on Tariffs and Trade (GATT). 73 For a time, the United States was a strong supporter of this approach to resolution of disharmonies among nations on intellectual property issues affecting software. The impetus for this seems to have slackened, however, after U.S. negotiators became aware of a lesser degree of consensus among U.S. software developers on certain key issues than they had thought was the case. Since the adoption of its directive on software copyright law, the European Community (EC) has begun pressing for international adoption of its position on a number of important software issues, including its copyright rule on decompilation of program code.

There is a clear need, given the international nature of the market for software, for a substantial international consensus on software protection issues. However, because there are so many hotly contested issues concerning the extent of copyright and the availability of patent protection for computer programs yet to be resolved, it may be premature to include very specific rules on these subjects in the GATT framework.

Prior to the adoption of the 1991 European Directive on the Protection of Computer Programs, there was general acceptance in Europe of copyright as a form of legal protection for computer programs. A number of nations had interpreted existing copyright statutes as covering programs. Others took legislative action to extend copyright protection to software. There was, however, some divergence in approach among the member nations of the EC in the interpretation of copyright law to computer software. 74

France, for example, although protecting programs under its copyright law, put software in the same category as industrial art, a category of work that is generally protected in Europe for 25 years instead of the life plus 50-year term that is the norm for literary and other artistic works. German courts concluded that to satisfy the "originality" standard of its copyright law, the author of a program needed to demonstrate that the program was the result of more than an average programmer's skill, a seemingly patentlike standard. In 'addition, Switzerland (a non-EC member but European nonetheless) nearly adopted an approach that treated both semiconductor chip designs and computer programs under a new copyright-like law.

Because of these differences and because it was apparent that computer programs would become an increasingly important item of commerce in the European Community, the EC undertook in the late 1980s to develop a policy concerning intellectual property protection for computer programs to which member nations should harmonize their laws. There was some support within the EC for creating a new law for the protection of software, but the directorate favoring a copyright approach won this internal struggle over what form of protection was appropriate for software.

In December 1988 the EC issued a draft directive on copyright protection for computer programs. This directive was intended to spell out in considerable detail in what respects member states should have uniform rules on copyright protection for programs. (The European civil law tradition generally prefers specificity in statutory formulations, in contrast with the U.S. common law tradition, which often prefers case-by-case adjudication of disputes as a way to fill in the details of a legal protection scheme.)

The draft directive on computer programs was the subject of intense debate within the European Community, as well as the object of some intense lobbying by major U.S. firms who were concerned about a number of issues, but particularly about what rule would be adopted concerning decompilation of program code and protection of the internal interfaces of

programs. Some U.S. firms, among them IBM Corp., strongly opposed any provision that would allow decompilation of program code and sought to have interfaces protected; other U.S. firms, such as Sun Microsystems, sought a rule that would permit decompilation and would deny protection to internal interfaces. 75

The final EC directive published in 1991 endorses the view that computer programs should be protected under member states' copyright laws as literary works and given at least 50 years of protection against unauthorized copying. 76 It permits decompilation of program code only if and to the extent necessary to obtain information to create an interoperable program. The inclusion in another program of information necessary to achieve interoperability seems, under the final directive, to be lawful.

The final EC directive states that "ideas" and "principles" embodied in programs are not protectable by copyright, but does not provide examples of what these terms might mean. The directive contains no exclusion from protection of such things as processes, procedures, methods of operation, and systems, as the U.S. statute provides. Nor does it clearly exclude protection of algorithms, interfaces, and program logic, as an earlier draft would have done. Rather, the final directive indicates that to the extent algorithms, logic, and interfaces are ideas, they are unprotectable by copyright law. In this regard, the directive seems, quite uncharacteristically for its civil law tradition, to leave much detail about how copyright law will be applied to programs to be resolved by litigation.

Having just finished the process of debating the EC directive about copyright protection of computer programs, intellectual property specialists in the EC have no interest in debating the merits of any sui generis approach to software protection, even though the only issue the EC directive really resolved may have been that of interoperability. Member states will likely have to address another controversial issue—whether or to what extent user interests in standardization of user interfaces should limit the scope of copyright

protection for programs—as they act on yet another EC directive, one that aims to standardize user interfaces of computer programs. Some U.S. firms may perceive this latter directive as an effort to appropriate valuable U.S. product features.

Japan was the first major industrialized nation to consider adoption of a sui generis approach to the protection of computer programs. 77 Its Ministry of International Trade and Industry (MITI) published a proposal that would have given 15 years of protection against unauthorized copying to computer programs that could meet a copyright-like originality standard under a copyright-like registration regime. MITI attempted to justify its proposed different treatment for computer programs as one appropriate to the different character of programs, compared with traditional copyrighted works. 78 The new legal framework was said to respond and be tailored to the special character of programs. American firms, however, viewed the MITI proposal, particularly its compulsory license provisions, as an effort by the Japanese to appropriate the valuable products of the U.S. software industry. Partly as a result of U.S. pressure, the MITI proposal was rejected by the Japanese government, and the alternative copyright proposal made by the ministry with jurisdiction over copyright law was adopted.

Notwithstanding their inclusion in copyright law, computer programs are a special category of protected work under Japanese law. Limiting the scope of copyright protection for programs is a provision indicating that program languages, rules, and algorithms are not protected by copyright law. 79 Japanese case law under this copyright statute has proceeded along lines similar to U.S. case law, with regard to exact and near-exact copying of program code and graphical aspects of videogame programs, 80 but there have been some Japanese court decisions interpreting the exclusion from protection provisions in a manner seemingly at odds with some U.S. Decisions.

The Tokyo High Court, for example, has opined that the processing flow of a program (an aspect of a program said to be protectable by U.S. law in the Whelan case) is an algorithm within the meaning of the copyright limitation provision. 81 Another seems to bear out Professor Karjala's prediction that Japanese courts would interpret the programming language limitation to permit firms to make compatible software. 82 There is one Japanese decision that can be read to prohibit reverse engineering of program code, but because this case involved not only disassembly of program code but also distribution of a clearly infringing program, the legality of intermediate copying to discern such things as interface information is unclear in Japan. 83

Other Nations

The United States has been pressing a number of nations to give "proper respect" to U.S. intellectual property products, including computer programs. In some cases, as in its dealings with the People's Republic of China, the United States has been pressing for new legislation to protect software under copyright law. In some cases, as in its dealings with Thailand, the United States has been pressing for more vigorous enforcement of intellectual property laws as they affect U.S. intellectual property products. In other cases, as in its dealings with Brazil, the United States pressed for repeal of sui generis legislation that disadvantaged U.S. software producers, compared with Brazilian developers. The United States has achieved some success in these efforts. Despite these successes, piracy of U.S.-produced software and other intellectual property products remains a substantial source of concern.

FUTURE CHALLENGES

Many of the challenges posed by use of existing intellectual property laws to protect computer programs have been discussed in previous sections. This may, however, only map the landscape of legal issues of widespread concern today. Below are some suggestions about issues as to which computer programs may present legal difficulties in the future.

Advanced Software Systems

It has thus far been exceedingly difficult for the legal system to resolve even relatively simple disputes about software intellectual property rights, such as those involved in the Lotus v. Paperback Software case. This does not bode well for how the courts are likely to deal with more complex problems presented by more complex software in future cases. The difficulties arise partly from the lack of familiarity of judges with the technical nature of computers and software, and partly from the lack of close analogies within the body of copyright precedents from which resolutions of software issues might be drawn. The more complex the software, the greater is the likelihood that specially trained judges will be needed to resolve intellectual property disputes about the software. Some advanced software systems are also likely to be sufficiently different from traditional kinds of copyrighted works that the analogical distance between the precedents and a software innovation may make it difficult to predict how copyright law should be applied to it. What copyright protection should be available, for example, to a user interface that responds to verbal commands, gestures, or movements of eyeballs?

Digital Media

The digital medium itself may require adaptation of the models underlying existing intellectual property systems. 84 Copyright law is built largely on the assumption that authors and publishers can control the manufacture and distribution of copies of protected works emanating from a central source. The ease with which digital works can be copied, redistributed, and used by multiple users, as well as the compactness and relative invisibility of works in digital form, have already created substantial incentives for developers of digital media products to focus their commercialization efforts on controlling the uses of digital works, rather than on the distribution of copies, as has more commonly been the rule in copyright industries.

Rules designed for controlling the production and distribution of copies may be difficult to adapt to a system in which uses need to be controlled. Some digital library and hypertext publishing systems seem to be designed to bypass copyright law (and its public policy safeguards, such as the fair use rule) and establish norms of use through restrictive access licensing

agreements. 85 Whether the law will eventually be used to regulate conditions imposed on access to these systems, as it has regulated access to such communication media as broadcasting, remains to be seen. However, the increasing convergence of intellectual property policy, broadcast and telecommunications policy, and other aspects of information policy seems inevitable.

There are already millions of people connected to networks of computers, who are thereby enabled to communicate with one another with relative ease, speed, and reliability. Plans are afoot to add millions more and to allow a wide variety of information services to those connected to the networks, some of which are commercial and some of which are noncommercial in nature. Because networks of this type and scope are a new phenomenon, it would seem quite likely that some new intellectual property issues will arise as the use of computer networks expands. The more commercial the uses of the networks, the more likely intellectual property disputes are to occur.

More of the content distributed over computer networks is copyrighted than its distributors seem to realize, but even as to content that has been recognized as copyrighted, there is a widespread belief among those who communicate over the net that at least noncommercial distributions of content—no matter the number of recipients—are "fair uses" of the content. Some lawyers would agree with this; others would not. Those responsible for the maintenance of the network may need to be concerned about potential liability until this issue is resolved.

A different set of problems may arise when commercial uses are made of content distributed over the net. Here the most likely disputes are those concerning how broad a scope of derivative work rights copyright owners should have. Some owners of copyrights can be expected to resist allowing anyone but themselves (or those licensed by them) to derive any financial benefit from creating a product or service that is built upon the value of their underlying work. Yet value-added services may be highly desirable to consumers, and the ability of outsiders to offer these products and services may spur beneficial competition. At the moment, the case law generally regards a copyright owner's derivative work right as infringed only if a recognizable block of expression is incorporated into another work. 86 How-

ever, the ability of software developers to provide value-added products and services that derive value from the underlying work without copying expression from it may lead some copyright owners to seek to extend the scope of derivative work rights.

Patents and Information Infrastructure of the Future

If patents are issued for all manner of software innovations, they are likely to play an important role in the development of the information infrastructure of the future. Patents have already been issued for hypertext navigation systems, for such things as latent semantic indexing algorithms, and for other software innovations that might be used in the construction of a new information infrastructure. Although it is easy to develop a list of the possible pros and cons of patent protection in this domain, as in the more general debate about software patents, it is worth noting that patents have not played a significant role in the information infrastructure of the past or of the present. How patents would affect the development of the new information infrastructure has not been given the study this subject may deserve.

Conflicts Between Information Haves and Have-Nots on an International Scale

When the United States was a developing nation and a net importer of intellectual property products, it did not respect copyright interests of any authors but its own. Charles Dickens may have made some money from the U.S. tours at which he spoke at public meetings, but he never made a dime from the publication of his works in the United States. Now that the United States is a developed nation and a net exporter of intellectual property products, its perspective on the rights of developing nations to determine for themselves what intellectual property rights to accord to the products of firms of the United States and other developed nations has changed. Given the greater importance nowadays of intellectual property products, both to the United States and to the world economy, it is foreseeable that there will be many occasions on which developed and developing nations will have disagreements on intellectual property issues.

The United States will face a considerable challenge in persuading other nations to subscribe to the same detailed rules that it has for dealing with intellectual property issues affecting computer programs. It may be easier for the United States to deter outright ''piracy" (unauthorized copying of the whole or substantially the whole of copyrighted works) of U.S. intellectual property products than to convince other nations that they must adopt the same rules as the United States has for protecting software.

It is also well for U.S. policymakers and U.S. firms to contemplate the possibility that U.S. firms may not always have the leading position in the world market for software products that they enjoy today. When pushing for very "strong" intellectual property protection for software today in the expectation that this will help to preserve the U.S. advantage in the world market, U.S. policymakers should be careful not to push for adoption of rules today that may substantially disadvantage them in the world market of the future if, for reasons not foreseen today, the United States loses the lead it currently enjoys in the software market.

As technological developments multiply around the globe—even as the patenting of human genes comes under serious discussion—nations, companies, and researchers find themselves in conflict over intellectual property rights (IPRs). Now, an international group of experts presents the first multidisciplinary look at IPRs in an age of explosive growth in science and technology.

This thought-provoking volume offers an update on current international IPR negotiations and includes case studies on software, computer chips, optoelectronics, and biotechnology—areas characterized by high development cost and easy reproducibility. The volume covers these and other issues:

• Modern economic theory as a basis for approaching international IPRs.
• U.S. intellectual property practices versus those in Japan, India, the European Community, and the developing and newly industrializing countries.
• Trends in science and technology and how they affect IPRs.
• Pros and cons of a uniform international IPRs regime versus a system reflecting national differences.

READ FREE ONLINE

Welcome to OpenBook!

You're looking at OpenBook, NAP.edu's online reading room since 1999. Based on feedback from you, our users, we've made some improvements that make it easier than ever to read thousands of publications on our website.

Do you want to take a quick tour of the OpenBook's features?

Show this book's table of contents , where you can jump to any chapter by name.

...or use these buttons to go back to the previous chapter or skip to the next one.

Jump up to the previous page or down to the next one. Also, you can type in a page number and press Enter to go directly to that page in the book.

Switch between the Original Pages , where you can read the report as it appeared in print, and Text Pages for the web version, where you can highlight and search the text.

To search the entire text of this book, type in your search term here and press Enter .

Share a link to this book page on your preferred social network or via email.

View our suggested citation for this chapter.

Ready to take your reading offline? Click here to buy this book in print or download it as a free PDF, if available.

Get Email Updates

Do you enjoy reading reports from the Academies online for free ? Sign up for email notifications and we'll let you know about new publications in your areas of interest when they're released.

• IEEE CS Standards
• Career Center
• Subscribe to Newsletter
• IEEE Standards

• For Industry Professionals
• For Students
• Launch a New Career
• Membership FAQ
• Membership FAQs
• Membership Grades
• Special Circumstances
• Discounts & Payments
• Distinguished Contributor Recognition
• Grant Programs
• Find a Local Chapter
• Find a Distinguished Visitor
• Find a Speaker on Early Career Topics
• Technical Communities
• Collabratec (Discussion Forum)
• Start a Chapter
• My Subscriptions
• My Referrals
• Computer Magazine
• ComputingEdge Magazine
• Let us help make your event a success. EXPLORE PLANNING SERVICES
• Events Calendar
• Calls for Papers
• Conference Proceedings
• Conference Highlights
• Top 2024 Conferences
• Conference Sponsorship Options
• Conference Planning Services
• Conference Organizer Resources
• Virtual Conference Guide
• Get a Quote
• CPS Dashboard
• CPS Author FAQ
• CPS Organizer FAQ
• Find the latest in advanced computing research. VISIT THE DIGITAL LIBRARY
• Open Access
• Tech News Blog
• Author Guidelines
• Reviewer Information
• Guest Editor Information
• Editor Information
• Editor-in-Chief Information
• Volunteer Opportunities
• Video Library
• Member Benefits
• Institutional Library Subscriptions
• Advertising and Sponsorship
• Code of Ethics
• Educational Webinars
• Online Education
• Certifications
• Industry Webinars & Whitepapers
• Research Reports
• Bodies of Knowledge
• CS for Industry Professionals
• Resource Library
• Newsletters
• Women in Computing
• Digital Library Access
• Organize a Conference
• Run a Publication
• Become a Distinguished Speaker
• Participate in Standards Activities
• Peer Review Content
• Author Resources
• Publish Open Access
• Society Leadership
• Boards & Committees
• Local Chapters
• Governance Resources
• Conference Publishing Services
• Chapter Resources
• About the Board of Governors
• Board of Governors Members
• Diversity & Inclusion
• Open Volunteer Opportunities
• Award Recipients
• Student Scholarships & Awards
• Nominate an Election Candidate
• Nominate a Colleague
• Corporate Partnerships
• Conference Sponsorships & Exhibits
• Advertising
• Recruitment
• Publications
• Education & Career

CiSE Case Studies in Translational Computer Science

Call for department articles.

CiSE ‘s newest department explores how findings in fundamental research in computer, computational, and data science translate to technologies, solutions, or practice for the benefit of science, engineering, and society. Specifically, each department article will highlight impactful translational research examples in which research has successfully moved from the laboratory to the field and into the community. The goal is to improve understanding of underlying approaches, explore challenges and lessons learned, with the overarching aim to formulate translational research processes that are broadly applicable.

Computing and data are increasingly essential to the research process across all areas of science and engineering and are key catalysts for impactful advances and breakthroughs. Consequently, translating fundamental advances in computer, computational, and data science help to ensure that these emerging insights, discoveries, and innovations are realized.  

Translational Research in Computer and Computational Sciences [1][2] refers the bridging of foundational and use-inspired (applied) research with the delivery and deployment of its outcomes to the target community, and supports bi-directional benefit in which delivery and deployment process informs the research. 

Call for Department Contributions: We seek short papers that align with our recommended structure and detail the following aspects of the described research:

• Overview: A description of the research, what problem does it address, who is the target user community, what are the key innovations and attributes, etc.
• Translation Process: What was the process used to move the research from the laboratory to the application? How were outcomes fed back into the research, and over what time period did this occur? How was the translation supported? 
• I mpact: What is the impact of the translated research, both on the CCDS research as well as the target domain(s)? 
• Lessons Learned: What are the lessons learned in terms of both the research and the translation process? What were the challenges faced?
• Conclusion: Based on your experience, do you have suggestions for processes or support structures that would have made the translation more effective?

CiSE Department articles are typically up to 3,000 words (including abstract, references, author biographies, and tables/figures [which count as 250 words each]), and are only reviewed by the department editors.

To pitch or submit a department article, please contact the editors directly by emailing:

• Manish Parashar  
• David Abramson  

Additional information for authors can be found here.

• D. Abramson and M. Parashar, “Translational Research in Computer Science,” Computer , vol. 52, no. 9, pp. 16-23, Sept. 2019, doi: 10.1109/MC.2019.2925650.
• D. Abramson, M. Parashar, and P. Arzberger. “Translation computer science – Overview of the special issue,” J. Computational Sci. , 2020, ISSN 1877-7503, https://www.sciencedirect.com/journal/journal-of-computational-science/special-issue/10P6T48JS7B.

Recommended by IEEE Computer Society

Master Data Management: Unlocking the Power of Data Excellence

User Management in the Enterprise: Technologies and Best Practices

7 Benefits of Adopting an Environmental Management System

CVPR Technical Program Features Presentations on the Latest AI and Computer Vision Research

Understanding Vector Databases: The Foundation of Modern AI Applications

How to Identify and Avoid ESG Greenwashing in Your Reports

What Is DevOps and How to Make It Work in an Organization

CVD 2024, A Recap by Prasenjit Banerjee

• Dean’s Office
• External Advisory Council
• Computing Council
• Extended Computing Council
• Undergraduate Advisory Group
• Break Through Tech AI
• Building 45 Event Space
• Infinite Mile Awards: Past Winners
• Frequently Asked Questions
• Undergraduate Programs
• Graduate Programs
• Educating Computing Bilinguals
• Online Learning
• Industry Programs
• AI Policy Briefs
• Envisioning the Future of Computing Prize
• SERC Symposium 2023
• SERC Case Studies
• SERC Scholars Program
• SERC Postdocs
• Common Ground Subjects
• For First-Year Students and Advisors
• For Instructors: About Common Ground Subjects
• Common Ground Award for Excellence in Teaching
• New and Incoming Faculty
• Faculty Resources
• Faculty Openings
• Search for: Search
• MIT Homepage

Case Studies in Social and Ethical Responsibilities of Computing

The MIT Case Studies in Social and Ethical Responsibilities of Computing (SERC) aims to advance new efforts within and beyond the Schwarzman College of Computing. The specially commissioned and peer-reviewed cases are brief and intended to be effective for undergraduate instruction across a range of classes and fields of study, and may also be of interest for computing professionals, policy specialists, and general readers. The series editors interpret “social and ethical responsibilities of computing” broadly. Some cases focus closely on particular technologies, others on trends across technological platforms. Others examine social, historical, philosophical, legal, and cultural facets that are essential for thinking critically about present-day efforts in computing activities. Special efforts are made to solicit cases on topics ranging beyond the United States and that highlight perspectives of people who are affected by various technologies in addition to perspectives of designers and engineers. New sets of case studies, produced with support from the MIT Press’ Open Publishing Services program, will be published twice a year and made available via the Knowledge Futures Group’s  PubPub  platform. The SERC case studies are made available for free on an open-access basis , under Creative Commons licensing terms. Authors retain copyright, enabling them to re-use and re-publish their work in more specialized scholarly publications. If you have suggestions for a new case study or comments on a published case, the series editors would like to hear from you! Please reach out to [email protected] .

Winter 2024

Integrals and Integrity: Generative AI Tries to Learn Cosmology

How Interpretable Is “Interpretable” Machine Learning?

AI’s Regimes of Representation: A Community-Centered Study of Text-to-Image Models in South Asia

Past issues, summer 2023.

Pretrial Risk Assessment on the Ground: Algorithms, Judgments, Meaning, and Policy by Cristopher Moore, Elise Ferguson, and Paul Guerin

To Search and Protect? Content Moderation and Platform Governance of Explicit Image Material by Mitali Thakor, Sumaiya Sabnam, Ransho Ueno, and Ella Zaslow

Winter 2023

Emotional Attachment to AI Companions and European Law by Claire Boine

Algorithmic Fairness in Chest X-ray Diagnosis: A Case Study by Haoran Zhang, Thomas Hartvigsen, and Marzyeh Ghassemi

The Right to Be an Exception to a Data-Driven Rule by Sarah H. Cen and Manish Raghavan

Twitter Gamifies the Conversation by C. Thi Nguyen, Meica Magnani, and Susan Kennedy

Summer 2022

“Porsche Girl”: When a Dead Body Becomes a Meme by Nadia de Vries

Patenting Bias: Algorithmic Race and Ethnicity Classifications, Proprietary Rights, and Public Data by Tiffany Nichols

Privacy and Paternalism: The Ethics of Student Data Collection by Kathleen Creel and Tara Dixit

Winter 2022

Differential Privacy and the 2020 US Census by Simson Garfinkel

The Puzzle of the Missing Robots by Suzanne Berger and Benjamin Armstrong

Protections for Human Subjects in Research: Old Models, New Needs? by Laura Stark

The Cloud Is Material: On the Environmental Impacts of Computation and Data Storage by Steven Gonzalez Monserrate

Algorithmic Redistricting and Black Representation in US Elections by Zachary Schutzman

Summer 2021

Hacking Technology, Hacking Communities: Codes of Conduct and Community Standards in Open Source by Christina Dunbar-Hester

Understanding Potential Sources of Harm throughout the Machine Learning Life Cycle by Harini Suresh and John Guttag

Identity, Advertising, and Algorithmic Targeting: Or How (Not) to Target Your “Ideal User” by Tanya Kant

Wrestling with Killer Robots: The Benefits and Challenges of Artificial Intelligence for National Security by Erik Lin-Greenberg

Public Debate on Facial Recognition Technologies in China by Tristan G. Brown, Alexander Statman, and Celine Sui

Winter 2021

The Case of the Nosy Neighbors by Johanna Gunawan and Woodrow Hartzog

Who Collects the Data? A Tale of Three Maps by Catherine D’Ignazio and Lauren Klein

The Bias in the Machine: Facial Recognition Technology and Racial Disparities by Sidney Perkowitz

The Dangers of Risk Prediction in the Criminal Justice System by Julia Dressel and Hany Farid

Sphero makes remarkably cool, programmable robots and STEAM-based educational tools that transform the way kids learn, create and invent through coding, science, music, and the arts.

• Lessons & Resources
• Schedule a Virtual Demo
• Professional Development
• Standards Alignment
• Funding & Grants
• Case Studies & Whitepapers
• App Downloads
• Sphero BOLT
• Sphero indi
• Sphero RVR+
• Sphero littleBits
• Sphero Blueprint
• Sphero Mini
• Computer Science Foundations (Sphero BOLT)
• Early Elementary
• Upper Elementary
• Middle School
• Past Year's Challenges

Enter your e-mail and password:

New customer? Create your account

Lost password? Recover password

Recover password

Enter your email:

Remembered your password? Back to login

Create my account

Please fill in the information below:

Already have an account? Login here

Case Studies & Whitepapers

Our library of insightful case studies and whitepapers is designed with educators in mind. We regularly publish original content to inform and educate on the importance and power of STEM education. Download the topics that interest you most and be the first to learn when new articles are published by signing up for our newsletter!

Sphero & littleBits K-12 STEAM & Computer Science Case Studies

Harlem Children's Zone Case Study

Improved Student Ambition, Resilience and Engagement

Making littleBits a Part of the Curriculum

Sphero & littlebits steam & computer science whitepapers.

Download our whitepapers to learn more about timely education topics, like why professional development is so critical for teachers or how to get STEM funding for your school or district.

Integrating Play-based Learning in a Hybrid Classroom to Accelerate STEM Education

While we’re all adjusting to working and learning in new ways this year, as an educator you may wonder why it's important to integrate play-based learning in a hybrid classroom or blended classroom. This whitepaper will dive deeper into what play-based learning is, the main benefits of play-based learning, and feature examples of PBL activities and lessons that yield the best outcome for students no matter where they are learning right now.

11 Reasons Why Professional Development is Critical for Teachers

In this whitepaper, we’ll look at professional development for teachers, why it matters, how it benefits not only teachers but their students, and how to discern you’re getting the best educational opportunities for your efforts.

7 Misconceptions About Introducing Computer Science to your Classroom

In this whitepaper, we'll dispel some of the most common myths. The goal is to give you a better understanding of why, perhaps now more than ever before, it's not only a good idea, but critical to teach your students the basics of computer science in and beyond in the classroom. (Hint: Their futures may depend on it.)

Guide to STEM Funding

When it comes to incorporating STEAM into the classroom, support can come from a surprising number of sources -- and sometimes, all you have to do is ask. That’s why we’ve compiled a list of resources to help drive innovative tech adoption.

Full STEAM Ahead!

Ready to get started with Sphero, littleBits or teaching STEAM and computer science in general? Our team of Education Experts is happy to answer any questions you may have.

• USF Research
• USF Libraries

Digital Commons @ USF > College of Engineering > Computer Science and Engineering > Theses and Dissertations

Computer Science and Engineering Theses and Dissertations

Theses/dissertations from 2023 2023.

Refining the Machine Learning Pipeline for US-based Public Transit Systems , Jennifer Adorno

Insect Classification and Explainability from Image Data via Deep Learning Techniques , Tanvir Hossain Bhuiyan

Brain-Inspired Spatio-Temporal Learning with Application to Robotics , Thiago André Ferreira Medeiros

Evaluating Methods for Improving DNN Robustness Against Adversarial Attacks , Laureano Griffin

Analyzing Multi-Robot Leader-Follower Formations in Obstacle-Laden Environments , Zachary J. Hinnen

Secure Lightweight Cryptographic Hardware Constructions for Deeply Embedded Systems , Jasmin Kaur

A Psychometric Analysis of Natural Language Inference Using Transformer Language Models , Antonio Laverghetta Jr.

Graph Analysis on Social Networks , Shen Lu

Deep Learning-based Automatic Stereology for High- and Low-magnification Images , Hunter Morera

Deciphering Trends and Tactics: Data-driven Techniques for Forecasting Information Spread and Detecting Coordinated Campaigns in Social Media , Kin Wai Ng Lugo

Automated Approaches to Enable Innovative Civic Applications from Citizen Generated Imagery , Hye Seon Yi

Theses/Dissertations from 2022 2022

Towards High Performing and Reliable Deep Convolutional Neural Network Models for Typically Limited Medical Imaging Datasets , Kaoutar Ben Ahmed

Task Progress Assessment and Monitoring Using Self-Supervised Learning , Sainath Reddy Bobbala

Towards More Task-Generalized and Explainable AI Through Psychometrics , Alec Braynen

A Multiple Input Multiple Output Framework for the Automatic Optical Fractionator-based Cell Counting in Z-Stacks Using Deep Learning , Palak Dave

On the Reliability of Wearable Sensors for Assessing Movement Disorder-Related Gait Quality and Imbalance: A Case Study of Multiple Sclerosis , Steven Díaz Hernández

Securing Critical Cyber Infrastructures and Functionalities via Machine Learning Empowered Strategies , Tao Hou

Social Media Time Series Forecasting and User-Level Activity Prediction with Gradient Boosting, Deep Learning, and Data Augmentation , Fred Mubang

A Study of Deep Learning Silhouette Extractors for Gait Recognition , Sneha Oladhri

Analyzing Decision-making in Robot Soccer for Attacking Behaviors , Justin Rodney

Generative Spatio-Temporal and Multimodal Analysis of Neonatal Pain , Md Sirajus Salekin

Secure Hardware Constructions for Fault Detection of Lattice-based Post-quantum Cryptosystems , Ausmita Sarker

Adaptive Multi-scale Place Cell Representations and Replay for Spatial Navigation and Learning in Autonomous Robots , Pablo Scleidorovich

Predicting the Number of Objects in a Robotic Grasp , Utkarsh Tamrakar

Humanoid Robot Motion Control for Ramps and Stairs , Tommy Truong

Preventing Variadic Function Attacks Through Argument Width Counting , Brennan Ward

Theses/Dissertations from 2021 2021

Knowledge Extraction and Inference Based on Visual Understanding of Cooking Contents , Ahmad Babaeian Babaeian Jelodar

Efficient Post-Quantum and Compact Cryptographic Constructions for the Internet of Things , Rouzbeh Behnia

Efficient Hardware Constructions for Error Detection of Post-Quantum Cryptographic Schemes , Alvaro Cintas Canto

Using Hyper-Dimensional Spanning Trees to Improve Structure Preservation During Dimensionality Reduction , Curtis Thomas Davis

Design, Deployment, and Validation of Computer Vision Techniques for Societal Scale Applications , Arup Kanti Dey

AffectiveTDA: Using Topological Data Analysis to Improve Analysis and Explainability in Affective Computing , Hamza Elhamdadi

Automatic Detection of Vehicles in Satellite Images for Economic Monitoring , Cole Hill

Analysis of Contextual Emotions Using Multimodal Data , Saurabh Hinduja

Data-driven Studies on Social Networks: Privacy and Simulation , Yasanka Sameera Horawalavithana

Automated Identification of Stages in Gonotrophic Cycle of Mosquitoes Using Computer Vision Techniques , Sherzod Kariev

Exploring the Use of Neural Transformers for Psycholinguistics , Antonio Laverghetta Jr.

Secure VLSI Hardware Design Against Intellectual Property (IP) Theft and Cryptographic Vulnerabilities , Matthew Dean Lewandowski

Turkic Interlingua: A Case Study of Machine Translation in Low-resource Languages , Jamshidbek Mirzakhalov

Automated Wound Segmentation and Dimension Measurement Using RGB-D Image , Chih-Yun Pai

Constructing Frameworks for Task-Optimized Visualizations , Ghulam Jilani Abdul Rahim Quadri

Trilateration-Based Localization in Known Environments with Object Detection , Valeria M. Salas Pacheco

Recognizing Patterns from Vital Signs Using Spectrograms , Sidharth Srivatsav Sribhashyam

Recognizing Emotion in the Wild Using Multimodal Data , Shivam Srivastava

A Modular Framework for Multi-Rotor Unmanned Aerial Vehicles for Military Operations , Dante Tezza

Human-centered Cybersecurity Research — Anthropological Findings from Two Longitudinal Studies , Anwesh Tuladhar

Learning State-Dependent Sensor Measurement Models To Improve Robot Localization Accuracy , Troi André Williams

Human-centric Cybersecurity Research: From Trapping the Bad Guys to Helping the Good Ones , Armin Ziaie Tabari

Theses/Dissertations from 2020 2020

Classifying Emotions with EEG and Peripheral Physiological Data Using 1D Convolutional Long Short-Term Memory Neural Network , Rupal Agarwal

Keyless Anti-Jamming Communication via Randomized DSSS , Ahmad Alagil

Active Deep Learning Method to Automate Unbiased Stereology Cell Counting , Saeed Alahmari

Composition of Atomic-Obligation Security Policies , Yan Cao Albright

Action Recognition Using the Motion Taxonomy , Maxat Alibayev

Sentiment Analysis in Peer Review , Zachariah J. Beasley

Spatial Heterogeneity Utilization in CT Images for Lung Nodule Classication , Dmitrii Cherezov

Feature Selection Via Random Subsets Of Uncorrelated Features , Long Kim Dang

Unifying Security Policy Enforcement: Theory and Practice , Shamaria Engram

PsiDB: A Framework for Batched Query Processing and Optimization , Mehrad Eslami

Composition of Atomic-Obligation Security Policies , Danielle Ferguson

Algorithms To Profile Driver Behavior From Zero-permission Embedded Sensors , Bharti Goel

The Efficiency and Accuracy of YOLO for Neonate Face Detection in the Clinical Setting , Jacqueline Hausmann

Beyond the Hype: Challenges of Neural Networks as Applied to Social Networks , Anthony Hernandez

Privacy-Preserving and Functional Information Systems , Thang Hoang

Managing Off-Grid Power Use for Solar Fueled Residences with Smart Appliances, Prices-to-Devices and IoT , Donnelle L. January

Novel Bit-Sliced In-Memory Computing Based VLSI Architecture for Fast Sobel Edge Detection in IoT Edge Devices , Rajeev Joshi

Edge Computing for Deep Learning-Based Distributed Real-time Object Detection on IoT Constrained Platforms at Low Frame Rate , Lakshmikavya Kalyanam

Establishing Topological Data Analysis: A Comparison of Visualization Techniques , Tanmay J. Kotha

Machine Learning for the Internet of Things: Applications, Implementation, and Security , Vishalini Laguduva Ramnath

System Support of Concurrent Database Query Processing on a GPU , Hao Li

Deep Learning Predictive Modeling with Data Challenges (Small, Big, or Imbalanced) , Renhao Liu

Countermeasures Against Various Network Attacks Using Machine Learning Methods , Yi Li

Towards Safe Power Oversubscription and Energy Efficiency of Data Centers , Sulav Malla

Design of Support Measures for Counting Frequent Patterns in Graphs , Jinghan Meng

Automating the Classification of Mosquito Specimens Using Image Processing Techniques , Mona Minakshi

Models of Secure Software Enforcement and Development , Hernan M. Palombo

Functional Object-Oriented Network: A Knowledge Representation for Service Robotics , David Andrés Paulius Ramos

Lung Nodule Malignancy Prediction from Computed Tomography Images Using Deep Learning , Rahul Paul

Algorithms and Framework for Computing 2-body Statistics on Graphics Processing Units , Napath Pitaksirianan

Efficient Viewshed Computation Algorithms On GPUs and CPUs , Faisal F. Qarah

Relational Joins on GPUs for In-Memory Database Query Processing , Ran Rui

Micro-architectural Countermeasures for Control Flow and Misspeculation Based Software Attacks , Love Kumar Sah

Efficient Forward-Secure and Compact Signatures for the Internet of Things (IoT) , Efe Ulas Akay Seyitoglu

Detecting Symptoms of Chronic Obstructive Pulmonary Disease and Congestive Heart Failure via Cough and Wheezing Sounds Using Smart-Phones and Machine Learning , Anthony Windmon

Toward Culturally Relevant Emotion Detection Using Physiological Signals , Khadija Zanna

Theses/Dissertations from 2019 2019

Beyond Labels and Captions: Contextualizing Grounded Semantics for Explainable Visual Interpretation , Sathyanarayanan Narasimhan Aakur

Empirical Analysis of a Cybersecurity Scoring System , Jaleel Ahmed

Phenomena of Social Dynamics in Online Games , Essa Alhazmi

A Machine Learning Approach to Predicting Community Engagement on Social Media During Disasters , Adel Alshehri

Interactive Fitness Domains in Competitive Coevolutionary Algorithm , ATM Golam Bari

Measuring Influence Across Social Media Platforms: Empirical Analysis Using Symbolic Transfer Entropy , Abhishek Bhattacharjee

A Communication-Centric Framework for Post-Silicon System-on-chip Integration Debug , Yuting Cao

Authentication and SQL-Injection Prevention Techniques in Web Applications , Cagri Cetin

Multimodal Emotion Recognition Using 3D Facial Landmarks, Action Units, and Physiological Data , Diego Fabiano

Robotic Motion Generation by Using Spatial-Temporal Patterns from Human Demonstrations , Yongqiang Huang

A GPU-Based Framework for Parallel Spatial Indexing and Query Processing , Zhila Nouri Lewis

A Flexible, Natural Deduction, Automated Reasoner for Quick Deployment of Non-Classical Logic , Trisha Mukhopadhyay

An Efficient Run-time CFI Check for Embedded Processors to Detect and Prevent Control Flow Based Attacks , Srivarsha Polnati

Force Feedback and Intelligent Workspace Selection for Legged Locomotion Over Uneven Terrain , John Rippetoe

Detecting Digitally Forged Faces in Online Videos , Neilesh Sambhu

Malicious Manipulation in Service-Oriented Network, Software, and Mobile Systems: Threats and Defenses , Dakun Shen

Advanced Search

• Email Notifications and RSS
• All Collections
• USF Faculty Publications
• Open Access Journals
• Conferences and Events
• Theses and Dissertations
• Textbooks Collection

Useful Links

• Rights Information
• SelectedWorks
• Submit Research

Home | About | Help | My Account | Accessibility Statement | Language and Diversity Statements

Privacy Copyright

• All Headlines

Top 40 Most Popular Case Studies of 2021

Two cases about Hertz claimed top spots in 2021's Top 40 Most Popular Case Studies

Two cases on the uses of debt and equity at Hertz claimed top spots in the CRDT’s (Case Research and Development Team) 2021 top 40 review of cases.

Hertz (A) took the top spot. The case details the financial structure of the rental car company through the end of 2019. Hertz (B), which ranked third in CRDT’s list, describes the company’s struggles during the early part of the COVID pandemic and its eventual need to enter Chapter 11 bankruptcy. 

The success of the Hertz cases was unprecedented for the top 40 list. Usually, cases take a number of years to gain popularity, but the Hertz cases claimed top spots in their first year of release. Hertz (A) also became the first ‘cooked’ case to top the annual review, as all of the other winners had been web-based ‘raw’ cases.

Besides introducing students to the complicated financing required to maintain an enormous fleet of cars, the Hertz cases also expanded the diversity of case protagonists. Kathyrn Marinello was the CEO of Hertz during this period and the CFO, Jamere Jackson is black.

Sandwiched between the two Hertz cases, Coffee 2016, a perennial best seller, finished second. “Glory, Glory, Man United!” a case about an English football team’s IPO made a surprise move to number four.  Cases on search fund boards, the future of malls,  Norway’s Sovereign Wealth fund, Prodigy Finance, the Mayo Clinic, and Cadbury rounded out the top ten.

Other year-end data for 2021 showed:

• Online “raw” case usage remained steady as compared to 2020 with over 35K users from 170 countries and all 50 U.S. states interacting with 196 cases.
• Fifty four percent of raw case users came from outside the U.S..
• The Yale School of Management (SOM) case study directory pages received over 160K page views from 177 countries with approximately a third originating in India followed by the U.S. and the Philippines.
• Twenty-six of the cases in the list are raw cases.
• A third of the cases feature a woman protagonist.
• Orders for Yale SOM case studies increased by almost 50% compared to 2020.
• The top 40 cases were supervised by 19 different Yale SOM faculty members, several supervising multiple cases.

CRDT compiled the Top 40 list by combining data from its case store, Google Analytics, and other measures of interest and adoption.

All of this year’s Top 40 cases are available for purchase from the Yale Management Media store .

And the Top 40 cases studies of 2021 are:

1.   Hertz Global Holdings (A): Uses of Debt and Equity

2.   Coffee 2016

3.   Hertz Global Holdings (B): Uses of Debt and Equity 2020

4.   Glory, Glory Man United!

5.   Search Fund Company Boards: How CEOs Can Build Boards to Help Them Thrive

6.   The Future of Malls: Was Decline Inevitable?

7.   Strategy for Norway's Pension Fund Global

8.   Prodigy Finance

9.   Design at Mayo

10. Cadbury

11. City Hospital Emergency Room

13. Volkswagen

14. Marina Bay Sands

15. Shake Shack IPO

16. Mastercard

17. Netflix

18. Ant Financial

19. AXA: Creating the New CR Metrics

20. IBM Corporate Service Corps

21. Business Leadership in South Africa's 1994 Reforms

22. Alternative Meat Industry

23. Children's Premier

24. Khalil Tawil and Umi (A)

25. Palm Oil 2016

26. Teach For All: Designing a Global Network

27. What's Next? Search Fund Entrepreneurs Reflect on Life After Exit

28. Searching for a Search Fund Structure: A Student Takes a Tour of Various Options

30. Project Sammaan

31. Commonfund ESG

32. Polaroid

33. Connecticut Green Bank 2018: After the Raid

34. FieldFresh Foods

35. The Alibaba Group

36. 360 State Street: Real Options

37. Herman Miller

38. AgBiome

39. Nathan Cummings Foundation

40. Toyota 2010

We use cookies to enhance our website for you. Proceed if you agree to this policy or learn more about it.

• Essay Database >
• Essays Samples >
• Essay Types >
• Case Study Example

Computer Science Case Studies Samples For Students

4 samples of this type

WowEssays.com paper writer service proudly presents to you a free directory of Computer Science Case Studies aimed to help struggling students tackle their writing challenges. In a practical sense, each Computer Science Case Study sample presented here may be a guide that walks you through the crucial stages of the writing procedure and showcases how to develop an academic work that hits the mark. Besides, if you require more visionary help, these examples could give you a nudge toward an original Computer Science Case Study topic or inspire a novice approach to a threadbare issue.

In case this is not enough to quench the thirst for effective writing help, you can request customized assistance in the form of a model Case Study on Computer Science crafted by a pro writer from scratch and tailored to your particular requirements. Be it a simple 2-page paper or an in-depth, extended piece, our writers specialized in Computer Science and related topics will deliver it within the stated period. Buy cheap essays or research papers now!

Example Of Engineering Case Study

Reform movement: demolishing barriers between computer science and computer engineering in purdue university, example of case study on structure of computer systems, structure of computer systems, free google case study example, the mission and value of google company.

The corporate culture and mission of Google Company reflects a philosophy of creating money without doing wickedness or evil. Moreover, work must be challenging and fun. At Google, these beliefs dictate life. The Google’s Inc certified mission statement is arranging or organizing information of the world and making it globally useful and accessible (Corporate Information 3). It has a value of no retaliation. Google Company forbids retaliation against any employee within the firm who participates in or reports an investigation enquiry of a likely violation of their code (Google’s Corporate Information 10).

History of the Google Company

Don't waste your time searching for a sample.

Get your case study done by professional writers!

Just from $10/page

Case Study On Cobol And Legacy Systems

Password recovery email has been sent to [email protected]

Use your new password to log in

You are not register!

By clicking Register, you agree to our Terms of Service and that you have read our Privacy Policy .

Now you can download documents directly to your device!

Check your email! An email with your password has already been sent to you! Now you can download documents directly to your device.

or Use the QR code to Save this Paper to Your Phone

The sample is NOT original!

Short on a deadline?

Don't waste time. Get help with 11% off using code - GETWOWED

No, thanks! I'm fine with missing my deadline

myCBSEguide

• Computer Science
• Class 12 Computer Science...

Class 12 Computer Science Case Study Questions

Table of Contents

myCBSEguide App

Download the app to get CBSE Sample Papers 2023-24, NCERT Solutions (Revised), Most Important Questions, Previous Year Question Bank, Mock Tests, and Detailed Notes.

You’ve come to the right site if you’re looking for diverse Class 12 Computer Science case study questions. We’ve put together a collection of Class 12 Computer Science case study questions for you on the myCBSEguide app and student dashboard .

As computer science becomes an increasingly popular field of study, more and more students are looking for resources to help them prepare for their exams. myCBSEguide is the only app that provides students with a variety of class 12 computer science case study questions. With over 1,000 questions to choose from, students can get the practice they need to ace their exams.

Significance of Class 12 Computer Science

Why is computer science so important? In a word, it’s because computers are everywhere. They are an integral part of our lives, and they are only going to become more so in the years to come. As such, it is essential that we understand how they work, and how to use them effectively.

Fascinating Subject

Computer science is a fascinating subject and one that can lead to a rewarding career in a variety of industries. So, if you’re considering CBSE class 12, be sure to give computer science a try.

Rapidly Growing Field

Computer science is the study of computational systems, their principles and their applications. It is a rapidly growing field that is constantly evolving, and as such, it is an essential part of any well-rounded education.

Critical Thinking and Problem-solving Skills

In CBSE class 12, computer science provides students with a strong foundation on which to build their future studies and careers. It equips them with the critical thinking and problem-solving skills they need to succeed in an increasingly digital world. Additionally, computer science is a great way to prepare for further study in fields such as engineering, business, and medicine.

Class 12 Computer Science

• Familiarize with the concepts of functions
• Become familiar with the creation and use of Python libraries.
• Become familiar with file management and using the file handling concept.
• Gain a basic understanding of the concept of efficiency in algorithms and computing.
• Capability to employ fundamental data structures such as stacks.
• Learn the fundamentals of computer networks, including the network stack, basic network hardware, basic protocols, and fundamental tools.
• Learn SQL aggregation functions by connecting a Python programme to a SQL database.

Case Study Questions in Class 12 Computer Science

There are several reasons why case study questions are included in class 12 computer science.

• First, class 12 computer science case study questions provide real-world examples of how computer science concepts can be applied in solving real-world problems.
• Second, they help students develop critical thinking and problem-solving skills. Third, they expose students to different computer science tools and techniques.
• Finally, case study questions help students understand the importance of collaboration and teamwork in computer science.

Class 12 Computer Science Case Study Questions Examples

The Central Board of Secondary Education (CBSE) has included case study questions in the class 12 computer science paper pattern. This move is in line with the board’s focus on practical and application-based learning. This move by the CBSE will help Class 12 Computer Science students to develop their analytical and problem-solving skills. It will also promote application-based learning, which is essential for Class 12 Computer Science students who want to pursue a career in computer science.

There are many apps out there that provide students with questions for their Class 12 computer science case study questions, but myCBSEguide is the only one that provides a variety of Class 12 Computer Science case study questions. Whether you’re a beginner or an expert, myCBSEguide has the perfect questions for you to practice with. With myCBSEguide, you can be sure that you’re getting the best possible preparation for your Class 12 computer science case studies. Here are a few examples of Class 12 computer science case study questions.

12 Computer Science case study question 1

Be Happy Corporation has set up its new centre at Noida, Uttar Pradesh for its office and web-based activities. It has 4 blocks of buildings.

The distance between the various blocks is as follows:

Numbers of computers in each block

(a) Suggest and draw the cable layout to efficiently connect various blocks of buildings within the Noida centre for connecting the digital devices.

(b) Suggest the placement of the following device with justification

(i) Repeater

(ii)Hub/Switch

Ans: Repeater: between C and D as the distance between them is 100 mts

Hub/ Switch : in each block as they help to share data packets within the devices of the network in each block

(c) Which kind of network (PAN/LAN/WAN) will be formed if the Noida office is connected to its head office in Mumbai?

(d) Which fast and very effective wireless transmission medium should preferably be used to connect the head office at Mumbai with the centre at Noida?

Ans: Satellite

12 Computer Science case study question 2

Rohit, a student of class 12th, is learning CSV File Module in Python. During examination, he has been assigned an incomplete python code (shown below) to create a CSV File ‘Student.csv’ (content shown below). Help him in completing the code which creates the desired CSV File.

1,AKSHAY,XII,A

2,ABHISHEK,XII,A

3,ARVIND,XII,A

4,RAVI,XII,A

5,ASHISH,XII,A

Incomplete Code

import_____ #Statement-1

fh = open(_____, _____, newline=”) #Statement-2

stuwriter = csv._____ #Statement-3

data.append(header)

for i in range(5):

roll_no = int(input(“Enter Roll Number : “))

name = input(“Enter Name : “)

Class = input(“Enter Class : “)

section = input(“Enter Section : “)

rec = [_____] #Statement-4

data.append(rec)

stuwriter. _____ (data) #Statement-5

• Identify the suitable code for blank space in line marked as Statement-1.
• a) csv file

Correct Answer : c) csv

• Identify the missing code for blank space in line marked as Statement-2?
• a) “School.csv”,”w”
• b) “Student.csv”,”w”
• c) “Student.csv”,”r”
• d) “School.csv”,”r”

Correct Answer : b) “Student.csv”,”w”

iii. Choose the function name (with argument) that should be used in the blank

space of line marked as Statement-3

• a) reader(fh)
• b) reader(MyFile)
• c) writer(fh)
• d) writer(MyFile)

Correct Answer : c) writer(fh)

• Identify the suitable code for blank space in line marked as Statement-4.
• a) ‘ROLL_NO’, ‘NAME’, ‘CLASS’, ‘SECTION’
• b) ROLL_NO, NAME, CLASS, SECTION
• c) ‘roll_no’,’name’,’Class’,’section’
• d) roll_no,name,Class,sectionc) co.connect()

Correct Answer : d) roll_no,name,Class,section

• Choose the function name that should be used in the blank space of line marked

as Statement-5 to create the desired CSV File?

• c) writerows()
• d) writerow()

Correct Answer : c) writerows()

12 Computer Science case study question 3

Krrishnav is looking for his dream job but has some restrictions. He loves Delhi and would take a job there if he is paid over Rs.40,000 a month. He hates Chennai and demands at least Rs. 1,00,000 to work there. In any another location he is willing to work for Rs. 60,000 a month. The following code shows his basic strategy for evaluating a job offer.

pay= _________

location= _________

if location == “Mumbai”:

print (“I’ll take it!”) #Statement 1

elif location == “Chennai”:

if pay < 100000:

print (“No way”) #Statement 2

print(“I am willing!”) #Statement 3

elif location == “Delhi” and pay > 40000:

print(“I am happy to join”) #Statement 4

elif pay > 60000:

print(“I accept the offer”) #Statement 5

print(“No thanks, I can find something

better”)#Statement 6

On the basis of the above code, choose the right statement which will be executed when different inputs for pay and location are given.

• Input: location = “Chennai”, pay = 50000
• Statement 1
• Statement 2
• Statement 3
• Statement 4

Correct Answer : ii. Statement 2

• Input: location = “Surat” ,pay = 50000
• Statement 5
• Statement 6

Correct Answer: d. Statement 6

iii. Input- location = “Any Other City”, pay = 1

a Statement 1

• Input location = “Delhi”, pay = 500000

Correct Answer: c. Statement 4

• Input- location = “Lucknow”, pay = 65000

iii. Statement 4

Correct Answer: d. Statement 5

Class 12 computer science case study examples provided above will help you to gain a better understanding. By working through the variety of Class 12 computer science case study examples, you will be able to see how the various concepts and techniques are applied in practice. This will give you a much better grasp of the material, and will enable you to apply the concepts to new problems.

myCBSEguide: A step towards success

myCBSEguide app is a one-stop solution for all your CBSE-related needs. It provides you with access to a wide range of study material, including sample papers, previous year papers, case study questions and mock tests. With the myCBSEguide app, you can also get personalized help and advice from our team of experts. So, what are you waiting for? Download the myCBSEguide app today and take a step towards success.

Test Generator

Create question paper PDF and online tests with your own name & logo in minutes.

Question Bank, Mock Tests, Exam Papers, NCERT Solutions, Sample Papers, Notes

Related Posts

• Competency Based Learning in CBSE Schools
• Class 11 Physical Education Case Study Questions
• Class 11 Sociology Case Study Questions
• Class 12 Applied Mathematics Case Study Questions
• Class 11 Applied Mathematics Case Study Questions
• Class 11 Mathematics Case Study Questions
• Class 11 Biology Case Study Questions
• Class 12 Physical Education Case Study Questions

2 thoughts on “Class 12 Computer Science Case Study Questions”

it is not good cause the questions are very wonted , yet easy to solve for a gay name Aditya kumari who resides in Numaligarh in Assam

Read More Computer Fundamental MCQ Questions

Leave a Comment

Save my name, email, and website in this browser for the next time I comment.

• Publications
• Conferences & Events
• Professional Learning
• Science Standards
• Awards & Competitions
• Instructional Materials
• Free Resources
• American Rescue Plan
• For Preservice Teachers

NCCSTS Case Collection

• Science and STEM Education Jobs
• Interactive eBooks+
• Digital Catalog
• Regional Product Representatives
• e-Newsletters
• Bestselling Books
• Latest Books
• Popular Book Series
• Prospective Authors
• Web Seminars
• Exhibits & Sponsorship
• Conference Reviewers
• National Conference • Denver 24
• Leaders Institute 2024
• National Conference • New Orleans 24
• Submit a Proposal
• Latest Resources
• Professional Learning Units & Courses
• For Districts
• Online Course Providers
• Schools & Districts
• College Professors & Students
• The Standards
• Teachers and Admin
• eCYBERMISSION
• Toshiba/NSTA ExploraVision
• Junior Science & Humanities Symposium
• Teaching Awards
• Climate Change
• Earth & Space Science
• New Science Teachers
• Early Childhood
• Middle School
• High School
• Postsecondary
• Informal Education
• Journal Articles
• Lesson Plans
• e-newsletters
• Science & Children
• Science Scope
• The Science Teacher
• Journal of College Sci. Teaching
• Connected Science Learning
• NSTA Reports
• Next-Gen Navigator
• Science Update
• Teacher Tip Tuesday
• Trans. Sci. Learning

MyNSTA Community

• My Collections

Case Study Listserv

Permissions & Guidelines

Submit a Case Study

Resources & Publications

Enrich your students’ educational experience with case-based teaching

The NCCSTS Case Collection, created and curated by the National Center for Case Study Teaching in Science, on behalf of the University at Buffalo, contains over a thousand peer-reviewed case studies on a variety of topics in all areas of science.

Cases (only) are freely accessible; subscription is required for access to teaching notes and answer keys.

Subscribe Today

Browse Case Studies

Latest Case Studies

Development of the NCCSTS Case Collection was originally funded by major grants to the University at Buffalo from the National Science Foundation , The Pew Charitable Trusts , and the U.S. Department of Education .

2023-2024 Academic Advising Guide Sheets

The goal of the academic advising guide sheets and sample plans of study is to promote undergraduate student success by guiding all students to timely completion of an undergraduate degree. Students are not limited to the course sequence provided for their academic program. Instead, the sample plan of study is one possible path to completing your degree and is meant to be used as a guide for planning purposes in consultation with an academic advisor. The plans also help students prepare for meetings with their academic advisor and track their progress in their selected academic program.

Students are encouraged to:

• Be familiar with required courses for their academic program and how they fit into the overall plan.
• Update their plans at the end of each semester.
• Review updated plans prior to advising appointments to identify which courses they need to take.
• Bring updated plans (hard copy or electronic) to all advising appointments.
• Refer to advising guide sheets as they explore majors and careers.

Your journey may not be the same as everybody else’s, and your academic advising guide sheet is one tool you can use to build your personal roadmap to success!

A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z