A Large-Scale Empirical Study of AI-Generated Code in Real-World Repositories
A large-scale study finds that in real-world software repositories, AI-assisted code differs only slightly from human-written code on code-level metrics, while revealing new patterns in commit size, stability, and code duplication.
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[Submitted on 28 Mar 2026 (v1), last revised 1 Jul 2026 (this version, v3)]
Title:A Large-Scale Comprehensive Measurement of AI-Generated Code in Real-World Repositories
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Abstract:Large language models (LLMs) are rapidly transforming software engineering by enabling developers to generate code ranging from small snippets to entire projects. As AI-assisted code becomes increasingly integrated into real-world systems, understanding its characteristics and impact is critical. Existing study on AI-generated code is usually limited in the lab setting with synthetic benchmarks and small-scale coding tasks and covers limited metrics. AI-assisted code's manifestation in real-world codebases and its differences between human-written one remain unclear. To close this gap, we perform a first large-scale measurement study of AI-assisted code, in comparison with the human-written, in real-world repositories. We study a comprehensive set of metrics including both code-level aspects (e.g., structural and graph-level complexity, coding style, security quality, etc.) and commit-level characteristics (e.g., commit size, frequency, post-commit stability, etc.). Our results provide new findings and insights: some contrast previous observations in the lab setting (e.g., we conclude that real-world AI-Human differences on code-level metrics are rather small instead of more pronounced), some extend prior results with finer-grained observations (e.g., the variance of security quality across different programming languages), yet more are presented for the first time on aspects not covered before (e.g., code duplication rate, commit size and stability, etc.). Based on these comprehensive real-world results, we also discuss the practical implications of AI-assisted programming.
Subjects:
Software Engineering (cs.SE)
Cite as: arXiv:2603.27130 [cs.SE]
(or arXiv:2603.27130v3 [cs.SE] for this version)
https://doi.org/10.48550/arXiv.2603.27130
arXiv-issued DOI via DataCite
Submission history
From: Tianhao Mao [view email] [v1] Sat, 28 Mar 2026 04:40:44 UTC (601 KB)
[v2] Fri, 3 Apr 2026 07:17:25 UTC (601 KB)
[v3] Wed, 1 Jul 2026 21:11:33 UTC (935 KB)
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