The Quiet Contributions: Insights into AI-Generated Silent Pull Requests
S M Mahedy Hasan, Md Fazle Rabbi, Minhaz Zibran
TL;DR
This study investigates AI-generated silent pull requests in Python repositories, analyzing their impact on code quality and security to understand acceptance or rejection reasons without accompanying comments.
Contribution
It provides the first empirical analysis of silent AI pull requests, exploring their effects on code complexity, quality, and security vulnerabilities.
Findings
Silent AI pull requests influence code complexity and security.
Analysis reveals potential indicators for acceptance or rejection.
Study covers 4,762 SPRs across multiple repositories.
Abstract
We present the first empirical study of AI-generated pull requests that are 'silent,' meaning no comments or discussions accompany them. This absence of any comments or discussions associated with such silent AI pull requests (SPRs) poses a unique challenge in understanding the rationale for their acceptance or rejection. Hence, we quantitatively study 4,762 SPRs of five AI agents made to popular Python repositories drawn from the AIDev public dataset. We examine SPRs impact on code complexity, other quality issues, and security vulnerabilities, especially to determine whether these insights can hint at the rationale for acceptance or rejection of SPRs.
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Taxonomy
TopicsEthics and Social Impacts of AI · Adversarial Robustness in Machine Learning · Artificial Intelligence in Healthcare and Education