Is GitHub Copilot an Asset or Liability? A Comprehensive Study Reveals the Truth
Table of Contents:
- Introduction
- What is GitHub Co-pilot?
- How Does Co-pilot Work?
- The Study Design
4.1 First Part: Fundamental Algorithmic Problems
4.1.1 Collecting Programming Tasks
4.1.2 Prompt Engineering
4.1.3 Evaluating Co-pilot's Responses
4.2 Second Part: Comparing Co-pilot with Human
4.2.1 Data Set and Methodology
4.2.2 Comparing Correct Solutions
4.2.3 Repairing Buggy Code
4.2.4 Diversity of Co-pilot's Solutions
- Experiments and Results
5.1 Co-pilot's Ability to Find Correct Solutions
5.2 Reproducibility of Co-pilot's Solutions
5.3 Comparing Co-pilot with Human Solutions
- Tips for Using Co-pilot Effectively
- Is Co-pilot an Asset or a Liability?
- Conclusion
- Highlights
- FAQs
Introduction
Hey everyone! In this article, we'll be discussing GitHub Co-pilot, an AI pair programmer that has been making waves in the software community. After OpenAI introduced Codex, based on their GPT-3 language model, Co-pilot emerged as a powerful tool for code generation. Its extensive training on vast amounts of code from GitHub and the internet allows it to provide code suggestions and completions in real-time, seamlessly integrated into popular IDEs like Visual Studio. While there have been numerous studies on how Co-pilot can be used to solve coding problems and complete programming tasks, we wanted to delve deeper into the question of whether relying on Co-pilot for code generation could be an asset or a liability for software projects and open-source contributions.
What is GitHub Co-pilot?
Co-pilot, an extension for popular IDEs like Visual Studio, is an AI-powered tool designed to assist developers by generating code suggestions based on problem prompts or even code blocks. It utilizes the powerful Codex model, which is pre-trained on an extensive dataset of code from various sources. Co-pilot provides developers with the top 10 solutions for a given prompt, making it a valuable resource for programmers to complete complex coding tasks.
How Does Co-pilot Work?
Co-pilot's workflow is simple yet effective. Developers can provide a problem Prompt to Co-pilot, which can be either a natural language description or a code block. Based on this prompt, Co-pilot generates Relevant code suggestions within the IDE. Co-pilot also offers the option to explore multiple suggestions for the same problem prompt, allowing developers to choose the most suitable solution. It leverages the token sequence within the suggestions to identify duplicates and ensure a diverse range of solutions.
The Study Design
To answer the question of whether using Co-pilot as an AI pair programmer is beneficial or detrimental, we conducted a comprehensive study with two main parts. In the first part, we focused on fundamental algorithmic problems, essential for assessing programming skills. We collected 20 tasks from a well-known book on algorithm design, spanning various categories like sorting, graph algorithms, and query algorithms. However, we had to perform prompt engineering to simplify the problem descriptions to ensure Co-pilot's comprehension.
In the Second part of the study, we compared Co-pilot's code solutions with human solutions. To do this, we used a dataset from a Python programming Course containing student submissions categorized as either correct, buggy, or irrelevant. Additionally, we employed a code-repairing tool called Factory to evaluate the fixability of Co-pilot's buggy code. We compared the diversity of solutions generated by Co-pilot and students, considering both the correctness and complexity of the code.
Experiments and Results
In our experiments, we found that Co-pilot was able to find the correct solution for approximately 50% of the algorithmic problems. Remarkably, 92% to 95% of the suggested solutions were optimal in terms of efficiency, even outperforming human solutions. However, Co-pilot's correct solution rate was lower compared to students. Nevertheless, the interesting discovery was that 95% of Co-pilot's buggy code could be repaired using the Factory tool. This indicated that while Co-pilot generated incorrect code at times, it was still fixable.
When comparing Co-pilot's diversity of solutions with students, we observed that students generally provided more diverse and Novel solutions. Although Co-pilot's solutions were less diverse, they were often more readable and understandable. Co-pilot's solutions tended to follow best practices, whereas students sometimes produced complex and less readable code. Our analysis also revealed that Co-pilot struggled with certain problem prompts, requiring more specific programming keywords to produce desired results.
Tips for Using Co-pilot Effectively
Based on our experience working with Co-pilot, we have compiled some useful tips to help developers make the most out of this AI pair programmer. Firstly, it is important to note that Co-pilot may struggle with understanding lengthy prompts. Therefore, breaking down complex problems into smaller, more explicit prompts can yield better results. Additionally, while Co-pilot excels in suggesting correct solutions, it is not effective at avoiding certain instructions. Hence, it is advisable to focus on instructing Co-pilot on what to do rather than what not to do.
Another crucial aspect is providing examples of the expected input types when writing function prompts for Co-pilot. This helps it better understand the intended behavior and produce accurate solutions. Moreover, developers should be aware that Co-pilot sometimes confuses the order or types of function arguments. Including examples of input and output formats can alleviate this issue.
Is Co-pilot an Asset or a Liability?
The question of whether Co-pilot is an asset or a liability ultimately depends on the expertise of the user and the project requirements. While Co-pilot can generate optimal and easily fixable code, it is not Flawless and can sometimes produce incorrect or complex code. Therefore, it is crucial for experts to scrutinize and filter the suggestions before integrating them into software projects or open-source contributions. Junior developers might benefit from using Co-pilot more cautiously, as it may offer less diverse solutions compared to human programmers.
Conclusion
In conclusion, GitHub Co-pilot, as an AI pair programmer, shows great potential in generating efficient and understandable code solutions. It can assist developers in solving complex programming problems and offering optimal solutions. However, challenges like incorrect code generation and limited solution diversity persist. By considering the expertise of the user, providing specific prompts, and filtering the output, Co-pilot can be a valuable asset for software projects. As language models like Co-pilot continue to evolve, it is essential to monitor their usage and evaluate their contributions effectively.
Highlights
- GitHub Co-pilot is an AI-powered tool designed to assist developers by generating code suggestions within popular IDEs like Visual Studio.
- Co-pilot's code suggestions are based on problem prompts or code blocks, leveraging the extensive training it received on vast amounts of code from various sources.
- Our study focused on fundamental algorithmic problems and compared Co-pilot's solutions with human solutions, considering correctness and complexity.
- Co-pilot's solutions were found to be optimal and easily fixable, even surpassing human solutions in terms of efficiency.
- Students provided more diverse solutions, but Co-pilot often generated more readable and understandable code, following best practices.
- Effective usage of Co-pilot requires specific prompts, instructing it on what to do, and providing examples of expected inputs.
- Co-pilot's value as an asset or a liability depends on the expertise of the user and the need for comprehensive code review before integration into projects.
FAQs
Q: Can Co-pilot find the correct solution for all algorithmic problems?
A: No, Co-pilot was able to find the correct solution for only about 50% of the algorithmic problems in our study. However, the solutions it did generate were often optimal.
Q: Can Co-pilot's solutions be reproduced consistently?
A: Yes, we found that Co-pilot was able to reproduce its correct solutions with an 86% success rate in subsequent runs.
Q: Are Co-pilot's solutions diverse?
A: While Co-pilot's solutions may lack diversity compared to human solutions, they often follow best practices and are more readable and understandable.
Q: Can Co-pilot generate buggy code?
A: Yes, Co-pilot can generate buggy code. However, we found that 95% of the buggy code generated by Co-pilot could be repaired using the Factory tool.
Q: Should I rely solely on Co-pilot for code generation?
A: It is not advisable to rely solely on Co-pilot for code generation. Expert review and code filtering are necessary to ensure the quality and correctness of the generated code.
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