AI-Powered Code Completion Tools and Their Impact on Developer Productivity

AI-powered code completion tools, such as GitHub Copilot, have revolutionized software development by automating repetitive coding tasks and providing real-time code suggestions. This paper explores the impact of these tools on developer productivity, focusing on their ability to reduce development time, improve code quality, and enhance overall efficiency. Through a review of existing literature, case studies, and experimental data, we analyze the benefits and challenges of AI-assisted coding. The findings reveal that developers using tools like GitHub Copilot complete tasks significantly faster, with a reported 55.8% reduction in completion time in controlled experiments . However, challenges such as dependency on AI-generated code, occasional inaccuracies, and security concerns remain. This paper concludes with recommendations for future enhancements and research directions to maximize the potential of AI-powered code completion tools in software development.

Authors

Publication Details

Journal
International Journal of Innovative Research in Science Engineering and Technology
Published
2025-03-20
DOI
https://doi.org/10.15680/ijirset.2025.1403120
Primary Topic
Model-Driven Software Engineering Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

AI-Powered Code Completion Tools and Their Impact on Developer Productivity

S. Anirudh, C K Gomathy
International Journal of Innovative Research in Science Engineering and Technology
Model-Driven Software Engineering Techniques
article

AI-Powered Code Completion Tools and Their Impact on Developer Productivity

S. Anirudh, C K Gomathy
article en

Abstract

AI-powered code completion tools, such as GitHub Copilot, have revolutionized software development by automating repetitive coding tasks and providing real-time code suggestions. This paper explores the impact of these tools on developer productivity, focusing on their ability to reduce development time, improve code quality, and enhance overall efficiency. Through a review of existing literature, case studies, and experimental data, we analyze the benefits and challenges of AI-assisted coding. The findings reveal that developers using tools like GitHub Copilot complete tasks significantly faster, with a reported 55.8% reduction in completion time in controlled experiments . However, challenges such as dependency on AI-generated code, occasional inaccuracies, and security concerns remain. This paper concludes with recommendations for future enhancements and research directions to maximize the potential of AI-powered code completion tools in software development.

International Journal of Innovative Research in Science Engineering and TechnologyVol. 14(03)
Openalex Percentile: Top 95%
Model-Driven Software Engineering Techniques
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