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

2026-01-28

AI Summary

This study addresses the challenge of evaluating AI-generated “silent” pull requests (SPRs)—submissions lacking developer comments or discussion—whose acceptance or rejection lacks transparent rationale. Leveraging the AIDev public dataset, we conduct the first systematic empirical analysis of 4,762 SPRs submitted by AI agents to popular Python repositories, quantitatively assessing their impact on code complexity, quality defects, and security vulnerabilities. Our findings uncover the underlying mechanisms driving the acceptance or rejection of SPRs, thereby filling a critical gap in evaluating AI-assisted development in the absence of interactive feedback. This work provides data-driven insights into the real-world implications of AI-generated code contributions, offering a foundation for more informed integration of AI agents into software engineering workflows.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• silent pull requests
• AI-generated code
• code review
• software quality
• acceptance rationale

Innovation

• silent pull requests
• AI-generated code
• empirical study
• code quality
• software security