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

Ruchika Pandey, Prabhat Singh, Raymond Wei, Shaila Shankar
6/25/2024
cs.SEcs.AI

No confident code implementation found for this paper. 3 possible matches can be shown below.

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 3 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Echoes of AI: Investigating the Downstream Effects of AI Assistants on Software Maintainability

87% match2025

The Impact of LLM-Assistants on Software Developer Productivity: A Systematic Literature Review

85% match2025

Generating Energy-Efficient Code via Large-Language Models -- Where are we now?

85% match2025

The Role of GitHub Copilot on Software Development: A Perspective on Productivity, Security, Best Practices and Future Directions

85% match2025

"My productivity is boosted, but ..." Demystifying Users' Perception on AI Coding Assistants

85% match2025

Green My LLM: Studying the key factors affecting the energy consumption of code assistants

84% match2024

Cite this paper

@article{pandey2024transforming,
  title  = {Transforming Software Development: Evaluating the Efficiency and Challenges of GitHub Copilot in Real-World Projects},
  author = {Ruchika Pandey and Prabhat Singh and Raymond Wei and Shaila Shankar},
  year   = {2024},
  eprint = {2406.17910},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2406.17910v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2406.17910.svg)](https://codeofpaper.com/paper/2406.17910)

Discussion