Enhancing Developer Productivity: a Study on GitHub Copilot's Code Completion Capabilities

EasyChair Preprint 15083

Abstract

As the complexity of software development increases, enhancing developer productivity has become a critical focus for organizations. This study investigates the impact of GitHub Copilot, an AI-powered code completion tool, on developer productivity. By employing a mixed-methods approach, we analyze quantitative data from surveys and productivity metrics, alongside qualitative insights from interviews with developers across various experience levels. The findings reveal that GitHub Copilot significantly enhances coding efficiency, reduces time spent on routine tasks, and improves code quality through intelligent suggestions. However, challenges such as dependency on AI-generated code and occasional inaccuracies in suggestions were also noted. This research contributes to the understanding of AI tools in software development, highlighting both their potential benefits and limitations. The implications for developers and organizations seeking to leverage AI technologies for improved productivity are discussed, along with recommendations for future research.

Keyphrases: AI technologies, AI-powered code, software development

@booklet{EasyChair:15083,
  author    = {Edwin Frank and Olaoye Godwin},
  title     = {Enhancing Developer Productivity: a Study on GitHub Copilot's Code Completion Capabilities},
  howpublished = {EasyChair Preprint 15083},
  year      = {EasyChair, 2024}}