GitHub Copilot: the perfect Code compLeeter?

Ilja Siroš, Dave Singelée, Bart Preneel·June 17, 2024

Summary

This paper evaluates GitHub Copilot's code generation quality using the LeetCode problem set for Java, C++, Python3, and Rust. The researchers analyzed over 50,000 submissions to assess Copilot's performance in terms of correctness, reliability, language dependency, problem difficulty, and efficiency. Copilot performed well in Java and C++, but faced challenges in Python3 and Rust, particularly with reliability. The study found that top-ranked suggestions were not always optimal, and Copilot generated more efficient code than average humans. It also revealed that Copilot's performance varied by problem topic and language, with higher accuracy in popular topics and better success in C++ and Java. The research suggests that while Copilot has potential as a code completion tool, further improvements are needed, especially for Python3 and addressing reproducibility issues.

Key findings

6

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to evaluate the quality of code generated by GitHub Copilot based on the LeetCode problem set using a custom automated framework. It assesses Copilot's reliability in code generation, correctness of generated code, dependency on programming language, problem difficulty level, and topic, as well as code's time and memory efficiency compared to human results . This research addresses the performance and functionality of Copilot's suggestions for programming problems, focusing on correctness, efficiency, and reliability . The study explores how Copilot's suggestions perform in terms of correctness, time, and memory efficiency, comparing them to human submissions and analyzing the impact of problem difficulty, programming language, and topic on correctness rates . The paper delves into the correctness of Copilot's solutions, the impact of problem difficulty on correctness, and the efficiency of Copilot-generated code compared to human submissions . The research also investigates the reliability, correctness, and efficiency of Copilot-generated code using a large dataset of LeetCode problems, evaluating suggestions for different difficulty levels and programming languages .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to the evaluation of GitHub Copilot's generated code quality based on the LeetCode problem set using a custom automated framework. The study evaluates Copilot's reliability in the code generation stage, the correctness of the generated code based on programming language, problem difficulty level, and topic, as well as the code's time and memory efficiency compared to human results . The research questions addressed include assessing Copilot's reliability, correctness of suggestions, and efficiency compared to human submissions . The study also explores the impact of programming language, problem difficulty, and topic on the correctness rate of Copilot's suggestions .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper on GitHub Copilot proposes several research questions and addresses them through empirical studies:

  • Reliability of Copilot in code generation: The paper investigates how often Copilot fails to generate code for a given problem and the average number of suggestions generated based on the programming language .
  • Correctness of Copilot's suggestions: It analyzes the correctness of Copilot's suggestions concerning the programming language, problem difficulty, suggestion rank, and problem topic .
  • Comparison with human performance: The paper compares the time and memory efficiency of Copilot's correct suggestions with the average human submission on LeetCode .
  • Security evaluation: Pearce et al. evaluated the security of Copilot-generated code by assessing the presence of the top 25 most dangerous software weaknesses in the generated code .
  • Productivity and code quality: Imai conducted an experiment comparing AI with human participants in software development productivity and code quality with and without Copilot .
  • Performance on algorithmic problems: Arghavan et al. studied Copilot's suggestions for fundamental algorithmic problems and compared them with human solutions in Python programming problems .

These analyses provide insights into the reliability, correctness, efficiency, security, productivity, and quality aspects of GitHub Copilot's code generation capabilities, contributing to the understanding of AI-assisted programming tools. The paper on GitHub Copilot provides insights into its characteristics and advantages compared to previous methods based on empirical evaluations and analyses:

  • Efficiency and Correctness: GitHub Copilot demonstrates high correctness rates in generating code solutions across various problem-solving categories such as Union Find, Recursion, and Shortest Path, with correctness percentages ranging from 60.0% to 88.5% . It also exhibits competitive time and memory efficiency compared to human submissions on platforms like LeetCode .
  • Coverage of Problem Categories: Copilot shows proficiency in generating solutions for a wide range of algorithmic problems, including Heap, Stack, Depth-First Search, and Greedy algorithms, with correctness rates varying from 52.8% to 81.8% .
  • Training and Improvement: The paper highlights the need for further training of Copilot on Python3 code to enhance its quality and correctness . Continuous research and evaluation are essential to verify the assumptions and progress of Copilot's performance .
  • Ranking of Suggestions: The study reveals that while the main suggestion (rank 0) often provides correct solutions, there is variance in the relative best rank across different programming languages, indicating the importance of exploring solutions from various ranks for optimal results .
  • Threats to Validity: The paper acknowledges potential threats to validity, such as the correctness of Copilot's solutions relying on LeetCode tests and the need for separate research to validate time and memory efficiency metrics . Recitation, a concern with large language models, is also highlighted as a potential challenge .

These findings underscore GitHub Copilot's strengths in code generation efficiency, correctness across diverse problem categories, and the importance of ongoing training and evaluation to enhance its performance and address potential limitations.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies have been conducted in the field of evaluating GitHub Copilot and its generated code quality. Noteworthy researchers in this area include Ilja Siroš, Dave Singelée, and Bart Preneel from COSIC, KU Leuven . Additionally, other researchers such as H. Pearce, B. Ahmad, B. Tan, B. Dolan-Gavitt, and R. Karri , S. Imai , and A. M. Dakhel, V. Majdinasab, A. Nikanjam, F. Khomh, M. C. Desmarais, Z. Ming, and Jiang have also contributed to the evaluation and analysis of GitHub Copilot.

The key to the solution mentioned in the paper evaluating GitHub Copilot's generated code quality is the comprehensive assessment of Copilot's reliability in the code generation phase, correctness of the generated code, dependency on the programming language, problem difficulty level, and problem topic. The study also focused on evaluating the time and memory efficiency of the generated code and comparing it to the average human results .


How were the experiments in the paper designed?

The experiments in the paper evaluating GitHub Copilot's generated code quality were designed to assess the reliability, correctness, and efficiency of Copilot-generated code using a custom automated framework . The experiments aimed to evaluate Copilot's performance across 1760 problems for each of the programming languages: Java, C++, Python3, and Rust . The study analyzed all of Copilot's suggestions, not just the top-ranked ones, resulting in over 50000 submissions to LeetCode over a 2-month period . The experiments focused on assessing the generated code's time and memory efficiency and comparing it to the average human results . Additionally, the study aimed to evaluate the impact of the problem's difficulty level and topic on the correctness rate of Copilot-generated solutions .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study of GitHub Copilot's generated code quality is LeetCode's free problem database . The code used in the study is open-source and available at the following GitHub repository: https://github.com/IljaSir/CopilotSolverForLeetCode .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that needed verification. The study extensively evaluated GitHub Copilot's generated code quality across different programming languages, problem difficulty levels, and topics . The research addressed key research questions regarding Copilot's reliability, correctness of suggestions, and time/memory efficiency compared to human submissions . The study analyzed a large dataset of over 50,000 submissions to LeetCode, demonstrating a comprehensive evaluation of Copilot's performance . Additionally, the paper discussed threats to validity, such as the correctness of Copilot's solutions, its performance compared to human submissions, and the time/memory efficiency metrics, providing a thorough analysis of potential limitations and considerations . The research went beyond existing studies by evaluating Copilot's reliability, correctness, and efficiency using a significant dataset containing a wide range of LeetCode problems, enhancing the robustness of the findings . The study's detailed analysis of Copilot's performance across different programming languages and problem types contributes to a comprehensive understanding of its capabilities and limitations, supporting the scientific hypotheses under investigation .


What are the contributions of this paper?

The contributions of the paper evaluating GitHub Copilot's generated code quality based on the LeetCode problem set include:

  • Assessing Copilot's reliability in the code generation phase across different programming languages .
  • Evaluating the correctness of Copilot's suggestions and how it varies based on the programming language, problem difficulty, suggestion rank, and problem topic .
  • Comparing the time and memory efficiency of Copilot's generated code to that of an average human submission .

What work can be continued in depth?

Further research can be conducted to delve deeper into the evaluation of GitHub Copilot's generated code. One area of focus could be to explore the impact of different programming languages on the reliability and correctness of Copilot's suggestions . Additionally, investigating how the difficulty level of a problem influences the correctness rate of Copilot's generated solutions could provide valuable insights . Moreover, analyzing the efficiency of Copilot-generated code in terms of time and memory performance compared to human submissions could be an interesting avenue for future studies .

Tables

5

Introduction
Background
Overview of GitHub Copilot and LeetCode
Importance of code generation tools in software development
Objective
To assess Copilot's performance in code generation
Identify strengths and weaknesses across languages
Methodology
Data Collection
Selection of problem set: LeetCode Java, C++, Python3, and Rust
Number of submissions: Over 50,000
Data source: GitHub Copilot-generated code and user submissions
Data Analysis
Correctness and Reliability
Analysis of correct and incorrect solutions
Reproducibility issues and error patterns
Language Dependency
Performance comparison across Java, C++, Python3, and Rust
Problem Difficulty
Impact on varying problem complexity
Efficiency
Comparison of generated code with human-written code
Efficiency metrics (time, memory usage)
Results
Performance by Language
Java and C++: High accuracy and efficiency
Python3 and Rust: Challenges in reliability and efficiency
Top-Ranked Suggestions
Optimal solutions: Frequency and accuracy
Limitations of top-ranked suggestions
Topic-Specific Performance
Variations in accuracy based on problem categories
Popular topics: Higher accuracy observed
Discussion
Implications for code completion tools
Potential improvements for Python3 and reproducibility
Future directions for research and development
Conclusion
Summary of findings and main takeaways
Limitations of the study and suggestions for future work
Implications for developers and the role of AI in coding assistance
Basic info
papers
software engineering
artificial intelligence
Advanced features
Insights
What languages does GitHub Copilot face challenges in according to the paper?
How many submissions were analyzed to assess Copilot's performance?
What were the main areas of assessment for Copilot's code generation quality as per the study?
In which programming languages did Copilot perform well as mentioned in the evaluation?

GitHub Copilot: the perfect Code compLeeter?

Ilja Siroš, Dave Singelée, Bart Preneel·June 17, 2024

Summary

This paper evaluates GitHub Copilot's code generation quality using the LeetCode problem set for Java, C++, Python3, and Rust. The researchers analyzed over 50,000 submissions to assess Copilot's performance in terms of correctness, reliability, language dependency, problem difficulty, and efficiency. Copilot performed well in Java and C++, but faced challenges in Python3 and Rust, particularly with reliability. The study found that top-ranked suggestions were not always optimal, and Copilot generated more efficient code than average humans. It also revealed that Copilot's performance varied by problem topic and language, with higher accuracy in popular topics and better success in C++ and Java. The research suggests that while Copilot has potential as a code completion tool, further improvements are needed, especially for Python3 and addressing reproducibility issues.
Mind map
Efficiency metrics (time, memory usage)
Comparison of generated code with human-written code
Impact on varying problem complexity
Performance comparison across Java, C++, Python3, and Rust
Reproducibility issues and error patterns
Analysis of correct and incorrect solutions
Popular topics: Higher accuracy observed
Variations in accuracy based on problem categories
Limitations of top-ranked suggestions
Optimal solutions: Frequency and accuracy
Python3 and Rust: Challenges in reliability and efficiency
Java and C++: High accuracy and efficiency
Efficiency
Problem Difficulty
Language Dependency
Correctness and Reliability
Data source: GitHub Copilot-generated code and user submissions
Number of submissions: Over 50,000
Selection of problem set: LeetCode Java, C++, Python3, and Rust
Identify strengths and weaknesses across languages
To assess Copilot's performance in code generation
Importance of code generation tools in software development
Overview of GitHub Copilot and LeetCode
Implications for developers and the role of AI in coding assistance
Limitations of the study and suggestions for future work
Summary of findings and main takeaways
Future directions for research and development
Potential improvements for Python3 and reproducibility
Implications for code completion tools
Topic-Specific Performance
Top-Ranked Suggestions
Performance by Language
Data Analysis
Data Collection
Objective
Background
Conclusion
Discussion
Results
Methodology
Introduction
Outline
Introduction
Background
Overview of GitHub Copilot and LeetCode
Importance of code generation tools in software development
Objective
To assess Copilot's performance in code generation
Identify strengths and weaknesses across languages
Methodology
Data Collection
Selection of problem set: LeetCode Java, C++, Python3, and Rust
Number of submissions: Over 50,000
Data source: GitHub Copilot-generated code and user submissions
Data Analysis
Correctness and Reliability
Analysis of correct and incorrect solutions
Reproducibility issues and error patterns
Language Dependency
Performance comparison across Java, C++, Python3, and Rust
Problem Difficulty
Impact on varying problem complexity
Efficiency
Comparison of generated code with human-written code
Efficiency metrics (time, memory usage)
Results
Performance by Language
Java and C++: High accuracy and efficiency
Python3 and Rust: Challenges in reliability and efficiency
Top-Ranked Suggestions
Optimal solutions: Frequency and accuracy
Limitations of top-ranked suggestions
Topic-Specific Performance
Variations in accuracy based on problem categories
Popular topics: Higher accuracy observed
Discussion
Implications for code completion tools
Potential improvements for Python3 and reproducibility
Future directions for research and development
Conclusion
Summary of findings and main takeaways
Limitations of the study and suggestions for future work
Implications for developers and the role of AI in coding assistance
Key findings
6

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to evaluate the quality of code generated by GitHub Copilot based on the LeetCode problem set using a custom automated framework. It assesses Copilot's reliability in code generation, correctness of generated code, dependency on programming language, problem difficulty level, and topic, as well as code's time and memory efficiency compared to human results . This research addresses the performance and functionality of Copilot's suggestions for programming problems, focusing on correctness, efficiency, and reliability . The study explores how Copilot's suggestions perform in terms of correctness, time, and memory efficiency, comparing them to human submissions and analyzing the impact of problem difficulty, programming language, and topic on correctness rates . The paper delves into the correctness of Copilot's solutions, the impact of problem difficulty on correctness, and the efficiency of Copilot-generated code compared to human submissions . The research also investigates the reliability, correctness, and efficiency of Copilot-generated code using a large dataset of LeetCode problems, evaluating suggestions for different difficulty levels and programming languages .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to the evaluation of GitHub Copilot's generated code quality based on the LeetCode problem set using a custom automated framework. The study evaluates Copilot's reliability in the code generation stage, the correctness of the generated code based on programming language, problem difficulty level, and topic, as well as the code's time and memory efficiency compared to human results . The research questions addressed include assessing Copilot's reliability, correctness of suggestions, and efficiency compared to human submissions . The study also explores the impact of programming language, problem difficulty, and topic on the correctness rate of Copilot's suggestions .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper on GitHub Copilot proposes several research questions and addresses them through empirical studies:

  • Reliability of Copilot in code generation: The paper investigates how often Copilot fails to generate code for a given problem and the average number of suggestions generated based on the programming language .
  • Correctness of Copilot's suggestions: It analyzes the correctness of Copilot's suggestions concerning the programming language, problem difficulty, suggestion rank, and problem topic .
  • Comparison with human performance: The paper compares the time and memory efficiency of Copilot's correct suggestions with the average human submission on LeetCode .
  • Security evaluation: Pearce et al. evaluated the security of Copilot-generated code by assessing the presence of the top 25 most dangerous software weaknesses in the generated code .
  • Productivity and code quality: Imai conducted an experiment comparing AI with human participants in software development productivity and code quality with and without Copilot .
  • Performance on algorithmic problems: Arghavan et al. studied Copilot's suggestions for fundamental algorithmic problems and compared them with human solutions in Python programming problems .

These analyses provide insights into the reliability, correctness, efficiency, security, productivity, and quality aspects of GitHub Copilot's code generation capabilities, contributing to the understanding of AI-assisted programming tools. The paper on GitHub Copilot provides insights into its characteristics and advantages compared to previous methods based on empirical evaluations and analyses:

  • Efficiency and Correctness: GitHub Copilot demonstrates high correctness rates in generating code solutions across various problem-solving categories such as Union Find, Recursion, and Shortest Path, with correctness percentages ranging from 60.0% to 88.5% . It also exhibits competitive time and memory efficiency compared to human submissions on platforms like LeetCode .
  • Coverage of Problem Categories: Copilot shows proficiency in generating solutions for a wide range of algorithmic problems, including Heap, Stack, Depth-First Search, and Greedy algorithms, with correctness rates varying from 52.8% to 81.8% .
  • Training and Improvement: The paper highlights the need for further training of Copilot on Python3 code to enhance its quality and correctness . Continuous research and evaluation are essential to verify the assumptions and progress of Copilot's performance .
  • Ranking of Suggestions: The study reveals that while the main suggestion (rank 0) often provides correct solutions, there is variance in the relative best rank across different programming languages, indicating the importance of exploring solutions from various ranks for optimal results .
  • Threats to Validity: The paper acknowledges potential threats to validity, such as the correctness of Copilot's solutions relying on LeetCode tests and the need for separate research to validate time and memory efficiency metrics . Recitation, a concern with large language models, is also highlighted as a potential challenge .

These findings underscore GitHub Copilot's strengths in code generation efficiency, correctness across diverse problem categories, and the importance of ongoing training and evaluation to enhance its performance and address potential limitations.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies have been conducted in the field of evaluating GitHub Copilot and its generated code quality. Noteworthy researchers in this area include Ilja Siroš, Dave Singelée, and Bart Preneel from COSIC, KU Leuven . Additionally, other researchers such as H. Pearce, B. Ahmad, B. Tan, B. Dolan-Gavitt, and R. Karri , S. Imai , and A. M. Dakhel, V. Majdinasab, A. Nikanjam, F. Khomh, M. C. Desmarais, Z. Ming, and Jiang have also contributed to the evaluation and analysis of GitHub Copilot.

The key to the solution mentioned in the paper evaluating GitHub Copilot's generated code quality is the comprehensive assessment of Copilot's reliability in the code generation phase, correctness of the generated code, dependency on the programming language, problem difficulty level, and problem topic. The study also focused on evaluating the time and memory efficiency of the generated code and comparing it to the average human results .


How were the experiments in the paper designed?

The experiments in the paper evaluating GitHub Copilot's generated code quality were designed to assess the reliability, correctness, and efficiency of Copilot-generated code using a custom automated framework . The experiments aimed to evaluate Copilot's performance across 1760 problems for each of the programming languages: Java, C++, Python3, and Rust . The study analyzed all of Copilot's suggestions, not just the top-ranked ones, resulting in over 50000 submissions to LeetCode over a 2-month period . The experiments focused on assessing the generated code's time and memory efficiency and comparing it to the average human results . Additionally, the study aimed to evaluate the impact of the problem's difficulty level and topic on the correctness rate of Copilot-generated solutions .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study of GitHub Copilot's generated code quality is LeetCode's free problem database . The code used in the study is open-source and available at the following GitHub repository: https://github.com/IljaSir/CopilotSolverForLeetCode .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that needed verification. The study extensively evaluated GitHub Copilot's generated code quality across different programming languages, problem difficulty levels, and topics . The research addressed key research questions regarding Copilot's reliability, correctness of suggestions, and time/memory efficiency compared to human submissions . The study analyzed a large dataset of over 50,000 submissions to LeetCode, demonstrating a comprehensive evaluation of Copilot's performance . Additionally, the paper discussed threats to validity, such as the correctness of Copilot's solutions, its performance compared to human submissions, and the time/memory efficiency metrics, providing a thorough analysis of potential limitations and considerations . The research went beyond existing studies by evaluating Copilot's reliability, correctness, and efficiency using a significant dataset containing a wide range of LeetCode problems, enhancing the robustness of the findings . The study's detailed analysis of Copilot's performance across different programming languages and problem types contributes to a comprehensive understanding of its capabilities and limitations, supporting the scientific hypotheses under investigation .


What are the contributions of this paper?

The contributions of the paper evaluating GitHub Copilot's generated code quality based on the LeetCode problem set include:

  • Assessing Copilot's reliability in the code generation phase across different programming languages .
  • Evaluating the correctness of Copilot's suggestions and how it varies based on the programming language, problem difficulty, suggestion rank, and problem topic .
  • Comparing the time and memory efficiency of Copilot's generated code to that of an average human submission .

What work can be continued in depth?

Further research can be conducted to delve deeper into the evaluation of GitHub Copilot's generated code. One area of focus could be to explore the impact of different programming languages on the reliability and correctness of Copilot's suggestions . Additionally, investigating how the difficulty level of a problem influences the correctness rate of Copilot's generated solutions could provide valuable insights . Moreover, analyzing the efficiency of Copilot-generated code in terms of time and memory performance compared to human submissions could be an interesting avenue for future studies .

Tables
5
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