Study of software developers' experience using the Github Copilot Tool in the software development process

• URL: http://arxiv.org/abs/2301.04991v1
• Date: Thu, 12 Jan 2023 13:12:54 GMT
• Title: Study of software developers' experience using the Github Copilot Tool in the software development process
• Authors: Mateusz Jaworski and Dariusz Piotrkowski
• Abstract summary: Github Copilot was announced on 29 June 2021. It uses trained model to generate code based on human understandable language. This research investigates developers' approach to this tool.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: In software development there is a constant pressure to produce code faster and faster without compromising on quality. New tools supporting developers are created in response to this demand. Currently a new generation of such solutions is about to be launched - Artificial Intelligence driven tools. On 29 June 2021 Github Copilot was announced. It uses trained model to generate code based on human understandable language. The focus of this research was to investigate software developers' approach to this tool. For this purpose a survey containing 18 questions was prepared and shared with programmers. A total of 42 answers were gathered. The results of the research indicate that developers' opinions are divided. Most of them met Github Copilot before attending the survey. The attitude to the tool was mostly positive but not many participants were willing to use it. Concerns are caused by security issues associated with using of Github Copilot.

Related papers

• Automated Code Review In Practice [1.6271516689052665]
  Several AI-assisted tools, such as Qodo, GitHub Copilot, and Coderabbit, provide automated reviews using large language models (LLMs) This study examines the impact of LLM-based automated code review tools in an industrial setting.
  arXiv  Detail & Related papers  (2024-12-24T16:24:45Z)
• Understanding Code Understandability Improvements in Code Reviews [79.16476505761582]
  We analyzed 2,401 code review comments from Java open-source projects on GitHub. 83.9% of suggestions for improvement were accepted and integrated, with fewer than 1% later reverted.
  arXiv  Detail & Related papers  (2024-10-29T12:21:23Z)
• OpenHands: An Open Platform for AI Software Developers as Generalist Agents [109.8507367518992]
  We introduce OpenHands, a platform for the development of AI agents that interact with the world in similar ways to a human developer. We describe how the platform allows for the implementation of new agents, safe interaction with sandboxed environments for code execution, and incorporation of evaluation benchmarks.
  arXiv  Detail & Related papers  (2024-07-23T17:50:43Z)
• 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)
• GitAgent: Facilitating Autonomous Agent with GitHub by Tool Extension [81.44231422624055]
  A growing area of research focuses on Large Language Models (LLMs) equipped with external tools capable of performing diverse tasks. In this paper, we introduce GitAgent, an agent capable of achieving the autonomous tool extension from GitHub.
  arXiv  Detail & Related papers  (2023-12-28T15:47:30Z)
• Exploring the Problems, their Causes and Solutions of AI Pair Programming: A Study on GitHub and Stack Overflow [6.724815667295355]
  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 473 GitHub issues, 706 GitHub discussions, and 142 Stack Overflow posts.
  arXiv  Detail & Related papers  (2023-11-02T06:24:38Z)
• 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)
• The GitHub Development Workflow Automation Ecosystems [47.818229204130596]
  Large-scale software development has become a highly collaborative endeavour. This chapter explores the ecosystems of development bots and GitHub Actions. It provides an extensive survey of the state-of-the-art in this domain.
  arXiv  Detail & Related papers  (2023-05-08T15:24:23Z)
• A Large-Scale Survey on the Usability of AI Programming Assistants: Successes and Challenges [23.467373994306524]
  In practice, developers do not accept AI programming assistants' initial suggestions at a high frequency. To understand developers' practices while using these tools, we administered a survey to a large population of developers. We found that developers are most motivated to use AI programming assistants because they help developers reduce key-strokes, finish programming tasks quickly, and recall syntax. We also found the most important reasons why developers do not use these tools are because these tools do not output code that addresses certain functional or non-functional requirements.
  arXiv  Detail & Related papers  (2023-03-30T03:21:53Z)
• Generation Probabilities Are Not Enough: Uncertainty Highlighting in AI Code Completions [54.55334589363247]
  We study whether conveying information about uncertainty enables programmers to more quickly and accurately produce code. We find that highlighting tokens with the highest predicted likelihood of being edited leads to faster task completion and more targeted edits.
  arXiv  Detail & Related papers  (2023-02-14T18:43:34Z)
• 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)

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.