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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 (SPRs) across five AI agents in popular Python repositories to understand why SPRs are accepted or rejected when no review discourse exists. By comparing before/after repository states using Radon for cyclomatic complexity, Pylint for code quality, and Semgrep for security vulnerabilities, the authors find that SPRs generally increase complexity and code quality issues, while most SPRs do not affect security posture. Crucially, accepted and rejected SPRs show remarkably similar impacts across these metrics, suggesting that these factors alone do not explain merger decisions. The work provides a replication package and highlights the need to consider additional factors such as project policies and reviewer behavior to predict SPR outcomes.

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.

The Quiet Contributions: Insights into AI-Generated Silent Pull Requests

TL;DR

This study investigates AI-generated silent pull requests (SPRs) across five AI agents in popular Python repositories to understand why SPRs are accepted or rejected when no review discourse exists. By comparing before/after repository states using Radon for cyclomatic complexity, Pylint for code quality, and Semgrep for security vulnerabilities, the authors find that SPRs generally increase complexity and code quality issues, while most SPRs do not affect security posture. Crucially, accepted and rejected SPRs show remarkably similar impacts across these metrics, suggesting that these factors alone do not explain merger decisions. The work provides a replication package and highlights the need to consider additional factors such as project policies and reviewer behavior to predict SPR outcomes.

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.
Paper Structure (17 sections, 4 figures, 2 tables)

This paper contains 17 sections, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Methodology
  3. Dataset
  4. Criteria for Measuring Impact
  5. Computing Cyclomatic Complexity:
  6. Capturing Code Quality Issues:
  7. Identification of Security Vulnerabilities:
  8. Computational Procedure
  9. Analysis and Findings
  10. Impact on Cyclomatic Complexity (RQ1)
  11. Impact on Other Code Quality Issues (RQ2)
  12. Most Frequent Code Quality Issues:
  13. Impact on Security Vulnerabilities (RQ3)
  14. Most Frequent Security Issues:
  15. Related Work
  16. ...and 2 more sections

Figures (4)

  • Figure 1: Accepted and rejected SPRs from each agent
  • Figure 2: AI agents' SPRs' impact on cyclomatic complexity
  • Figure 3: AI agents' SPRs' impact on code quality issues
  • Figure 4: AI agents' SPRs' impact on security vulnerabilities