Transforming Software Development: Evaluating the Efficiency and Challenges of GitHub Copilot in Real-World Projects

• URL: http://arxiv.org/abs/2406.17910v1
• Date: Tue, 25 Jun 2024 19:51:21 GMT
• Title: Transforming Software Development: Evaluating the Efficiency and Challenges of GitHub Copilot in Real-World Projects
• Authors: Ruchika Pandey, Prabhat Singh, Raymond Wei, Shaila Shankar,
• Abstract summary: GitHub Copilot is an AI-powered coding assistant. This study evaluates the efficiency gains, areas for improvement, and emerging challenges of using GitHub Copilot.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative AI technologies promise to transform the product development lifecycle. This study evaluates the efficiency gains, areas for improvement, and emerging challenges of using GitHub Copilot, an AI-powered coding assistant. We identified 15 software development tasks and assessed Copilot's benefits through real-world projects on large proprietary code bases. Our findings indicate significant reductions in developer toil, with up to 50% time saved in code documentation and autocompletion, and 30-40% in repetitive coding tasks, unit test generation, debugging, and pair programming. However, Copilot struggles with complex tasks, large functions, multiple files, and proprietary contexts, particularly with C/C++ code. We project a 33-36% time reduction for coding-related tasks in a cloud-first software development lifecycle. This study aims to quantify productivity improvements, identify underperforming scenarios, examine practical benefits and challenges, investigate performance variations across programming languages, and discuss emerging issues related to code quality, security, and developer experience.

Related papers

• The Impact of Generative AI on Collaborative Open-Source Software Development: Evidence from GitHub Copilot [4.8256226973915455]
  We investigate the role of GitHub Copilot, a generative AI programmer pair, on software development in open-source community. We find that Copilot significantly enhances project-level productivity by 6.5%. We conclude that AI pair programmers bring benefits to developers to automate and augment their code, but human developers' knowledge of software projects can enhance the benefits.
  arXiv  Detail & Related papers  (2024-10-02T23:26:10Z)
• Benchmarking ChatGPT, Codeium, and GitHub Copilot: A Comparative Study of AI-Driven Programming and Debugging Assistants [0.0]
  Large language models (LLMs) have become essential for tasks like code generation, bug fixing, and optimization. This paper presents a comparative study of ChatGPT, Codeium, and GitHub Copilot, evaluating their performance on LeetCode problems.
  arXiv  Detail & Related papers  (2024-09-30T03:53:40Z)
• Does Co-Development with AI Assistants Lead to More Maintainable Code? A Registered Report [6.7428644467224]
  This study aims to examine the influence of AI assistants on software maintainability. In Phase 1, developers will add a new feature to a Java project, with or without the aid of an AI assistant. In Phase 2, a randomized controlled trial, will involve a different set of developers evolving random Phase 1 projects - working without AI assistants.
  arXiv  Detail & Related papers  (2024-08-20T11:48:42Z)
• Agent-Driven Automatic Software Improvement [55.2480439325792]
  This research proposal aims to explore innovative solutions by focusing on the deployment of agents powered by Large Language Models (LLMs) The iterative nature of agents, which allows for continuous learning and adaptation, can help surpass common challenges in code generation. We aim to use the iterative feedback in these systems to further fine-tune the LLMs underlying the agents, becoming better aligned to the task of automated software improvement.
  arXiv  Detail & Related papers  (2024-06-24T15:45:22Z)
• 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)
• A Study on Developer Behaviors for Validating and Repairing LLM-Generated Code Using Eye Tracking and IDE Actions [13.58143103712]
  GitHub Copilot is a large language model (LLM)-powered code generation tool. This paper investigates how developers validate and repair code generated by Copilot. Being aware of the code's provenance led to improved performance, increased search efforts, more frequent Copilot usage, and higher cognitive workload.
  arXiv  Detail & Related papers  (2024-05-25T06:20:01Z)
• DevBench: A Comprehensive Benchmark for Software Development [72.24266814625685]
  DevBench is a benchmark that evaluates large language models (LLMs) across various stages of the software development lifecycle. Empirical studies show that current LLMs, including GPT-4-Turbo, fail to solve the challenges presented within DevBench. Our findings offer actionable insights for the future development of LLMs toward real-world programming applications.
  arXiv  Detail & Related papers  (2024-03-13T15:13:44Z)
• DevEval: Evaluating Code Generation in Practical Software Projects [52.16841274646796]
  We propose a new benchmark named DevEval, aligned with Developers' experiences in practical projects. DevEval is collected through a rigorous pipeline, containing 2,690 samples from 119 practical projects. We assess five popular LLMs on DevEval and reveal their actual abilities in code generation.
  arXiv  Detail & Related papers  (2024-01-12T06:51:30Z)
• 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)
• Breaks and Code Quality: Investigating the Impact of Forgetting on Software Development. A Registered Report [15.438443553618896]
  It is crucial to ensure that developers have a clear understanding of the and can work efficiently and effectively even after long interruptions. This registered report proposes an empirical study aimed at investigating the impact of the developer's activity breaks duration and different code quality properties.
  arXiv  Detail & Related papers  (2023-05-01T10:33:17Z)
• Competition-Level Code Generation with AlphaCode [74.87216298566942]
  We introduce AlphaCode, a system for code generation that can create novel solutions to problems that require deeper reasoning. In simulated evaluations on recent programming competitions on the Codeforces platform, AlphaCode achieved on average a ranking of top 54.3%.
  arXiv  Detail & Related papers  (2022-02-08T23:16:31Z)

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.