Understanding the Rejection of Fixes Generated by Agentic Pull Requests -- Insights from the AIDev Dataset

2025-12-27 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Robotics & Autonomous Systems · Depth: Expert, long

Summary

AI coding agents like Copilot, Devin, Cursor, and Claude generate pull requests (PRs) for code fixes, but 46.41% of these are rejected, as revealed by an exploration of the AIDev dataset. This study, published on January 19, 2026, conducted a qualitative analysis of 306 non-merged agent-created PRs, identifying 14 rejection reasons across four categories: relevance, implementation, technical, and provider-related issues. Relevance issues, such as inactive or superseded PRs, are most common. Implementation problems include incorrect fixes or inappropriate approaches. Technical issues involve CI failures and breaking changes, while provider issues stem from agent unreachability or rate limits. Rejected PRs also incur substantial wasted effort, with median code churn ranging from 81 to 293 lines and 1 to 4.5 review comments.

Key takeaway

For Machine Learning Engineers or Software Engineers integrating AI coding agents, you should provide explicit guidance on acceptable fix approaches and instruct agents on how to validate changes against CI pipelines and tests. Prioritize tasks carefully to avoid assigning low-priority issues to agents, which often lead to wasted human review efforts and agent resources. Your clear instructions and task management will significantly improve agent-generated fix acceptance rates.

Key insights

46.41% of AI agent-generated code fixes are rejected due to relevance, implementation, technical, or provider issues.

Principles

• AI agent fixes often fail due to lack of context.
• Prioritize tasks to avoid wasted agent resources.
• Clear guidance improves agent fix acceptance.

Method

A qualitative study analyzed 306 rejected AI-agent pull requests from the AIDev dataset to identify 14 rejection reasons, categorized into four high-level themes. This was followed by a quantitative analysis of code churn and comments.

In practice

• Define explicit constraints for AI agent approaches.
• Instruct agents on CI validation and testing.
• Avoid assigning low-priority tasks to agents.

Topics

• AI Coding Agents
• Pull Request Rejection
• Software Development
• Code Fixes
• AIDev Dataset
• Continuous Integration

Code references

• 567-labs/instructor
• neondatabase/autoscaling
• bespokelabsai/curator
• ruvnet/claude-flow
• Azure/Azure-Sentinel

Best for: AI Engineer, Research Scientist, VP of Engineering/Data, AI Scientist, Machine Learning Engineer, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.