Exploring the Problems, their Causes and Solutions of AI Pair Programming: A Study with Practitioners of GitHub Copilot

• URL: http://arxiv.org/abs/2311.01020v2
• Date: Sun, 28 Apr 2024 13:47:20 GMT
• Title: Exploring the Problems, their Causes and Solutions of AI Pair Programming: A Study with Practitioners of GitHub Copilot
• Authors: Xiyu Zhou, Peng Liang, Beiqi Zhang, Zengyang Li, Aakash Ahmad, Mojtaba Shahin, Muhammad Waseem,
• Abstract summary: GitHub Copilot, the AI programmer pair, utilize machine learning models trained on a large corpus of code snippets to generate code suggestions. Despite its popularity in software development, there is limited empirical evidence on the actual experiences of practitioners who work with Copilot. We collected data from 476 GitHub issues, 706 GitHub discussions, and 142 Stack Overflow posts.
• Score: 6.724815667295355
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the recent advancement of Artificial Intelligence (AI) and Large Language Models (LLMs), AI-based code generation tools become a practical solution for software development. GitHub Copilot, the AI pair programmer, utilizes machine learning models trained on a large corpus of code snippets to generate code suggestions using natural language processing. Despite its popularity in software development, there is limited empirical evidence on the actual experiences of practitioners who work with Copilot. To this end, we conducted an empirical study to understand the problems that practitioners face when using Copilot, as well as their underlying causes and potential solutions. We collected data from 476 GitHub issues, 706 GitHub discussions, and 142 Stack Overflow posts. Our results reveal that (1) Operation Issue and Compatibility Issue are the most common problems faced by Copilot users, (2) Copilot Internal Error, Network Connection Error, and Editor/IDE Compatibility Issue are identified as the most frequent causes, and (3) Bug Fixed by Copilot, Modify Configuration/Setting, and Use Suitable Version are the predominant solutions. Based on the results, we discuss the potential areas of Copilot for enhancement, and provide the implications for the Copilot users, the Copilot team, and researchers.

Related papers

• Impact of the Availability of ChatGPT on Software Development: A Synthetic Difference in Differences Estimation using GitHub Data [49.1574468325115]
  ChatGPT is an AI tool that enhances software production efficiency. We estimate ChatGPT's effects on the number of git pushes, repositories, and unique developers per 100,000 people. These results suggest that AI tools like ChatGPT can substantially boost developer productivity, though further analysis is needed to address potential downsides such as low quality code and privacy concerns.
  arXiv  Detail & Related papers  (2024-06-16T19:11:15Z)
• OS-Copilot: Towards Generalist Computer Agents with Self-Improvement [48.29860831901484]
  We introduce OS-Copilot, a framework to build generalist agents capable of interfacing with comprehensive elements in an operating system (OS) We use OS-Copilot to create FRIDAY, a self-improving embodied agent for automating general computer tasks. On GAIA, a general AI assistants benchmark, FRIDAY outperforms previous methods by 35%, showcasing strong generalization to unseen applications via accumulated skills from previous tasks.
  arXiv  Detail & Related papers  (2024-02-12T07:29:22Z)
• Exploring the Effect of Multiple Natural Languages on Code Suggestion Using GitHub Copilot [46.822148186169144]
  GitHub Copilot is an AI-enabled tool that automates program synthesis. Recent studies have extensively examined Copilot's capabilities in various programming tasks. However, little is known about the effect of different natural languages on code suggestion.
  arXiv  Detail & Related papers  (2024-02-02T14:30:02Z)
• SWE-bench: Can Language Models Resolve Real-World GitHub Issues? [80.52201658231895]
  SWE-bench is an evaluation framework consisting of $2,294$ software engineering problems drawn from real GitHub issues and corresponding pull requests across $12$ popular Python repositories. We show that both state-of-the-art proprietary models and our fine-tuned model SWE-Llama can resolve only the simplest issues.
  arXiv  Detail & Related papers  (2023-10-10T16:47:29Z)
• Demystifying Practices, Challenges and Expected Features of Using GitHub Copilot [3.655281304961642]
  We conducted an empirical study by collecting and analyzing the data from Stack Overflow (SO) and GitHub Discussions. We identified the programming languages, technologies used with Copilot, functions implemented, benefits, limitations, and challenges when using Copilot. Our results suggest that using Copilot is like a double-edged sword, which requires developers to carefully consider various aspects when deciding whether or not to use it.
  arXiv  Detail & Related papers  (2023-09-11T16:39:37Z)
• Automatic Root Cause Analysis via Large Language Models for Cloud Incidents [51.94361026233668]
  We introduce RCACopilot, an on-call system empowered by a large language model for automating root cause analysis of cloud incidents. RCACopilot matches incoming incidents to corresponding incident handlers based on their alert types, aggregates the critical runtime diagnostic information, predicts the incident's root cause category, and provides an explanatory narrative. We evaluate RCACopilot using a real-world dataset consisting of a year's worth of incidents from Microsoft.
  arXiv  Detail & Related papers  (2023-05-25T06:44:50Z)
• Measuring the Runtime Performance of Code Produced with GitHub Copilot [1.6021036144262577]
  We evaluate the runtime performance of code produced when developers use GitHub Copilot versus when they do not. Our results suggest that using Copilot may produce code with a significantly slower runtime performance.
  arXiv  Detail & Related papers  (2023-05-10T20:14:52Z)
• Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Language [3.155277175705079]
  GitHub Copilot is an artificial intelligence model for automatically generating source code from natural language problem descriptions. Since June 2022, Copilot has officially been available for free to all students as a plug-in to development environments like Visual Studio Code.
  arXiv  Detail & Related papers  (2022-10-27T03:48:24Z)
• GitHub Copilot AI pair programmer: Asset or Liability? [14.572381978575182]
  We study the capabilities of Copilot in two different programming tasks. We compare Copilot's proposed solutions with those of human programmers on a set of programming tasks. The results show that Copilot is capable of providing solutions for almost all fundamental algorithmic problems.
  arXiv  Detail & Related papers  (2022-06-30T15:00:03Z)
• An Empirical Cybersecurity Evaluation of GitHub Copilot's Code Contributions [8.285068188878578]
  GitHub Copilot is a language model trained over open-source GitHub code. Code often contains bugs - and so, it is certain that the language model will have learned from exploitable, buggy code. This raises concerns on the security of Copilot's code contributions.
  arXiv  Detail & Related papers  (2021-08-20T17:30:33Z)
• Measuring Coding Challenge Competence With APPS [54.22600767666257]
  We introduce APPS, a benchmark for code generation. Our benchmark includes 10,000 problems, which range from having simple one-line solutions to being substantial algorithmic challenges. Recent models such as GPT-Neo can pass approximately 15% of the test cases of introductory problems.
  arXiv  Detail & Related papers  (2021-05-20T17:58:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.