Analyzing Message-Code Inconsistency in AI Coding Agent-Authored Pull Requests
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866908788183793664 |
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| author | Gong, Jingzhi Pinna, Giovanni Bian, Yixin Zhang, Jie M. |
| author_facet | Gong, Jingzhi Pinna, Giovanni Bian, Yixin Zhang, Jie M. |
| contents | Pull request (PR) descriptions generated by AI coding agents are the primary channel for communicating code changes to human reviewers. However, the alignment between these messages and the actual changes remains unexplored, raising concerns about the trustworthiness of AI agents. To fill this gap, we analyzed 23,247 agentic PRs across five agents using PR message-code inconsistency (PR-MCI). We contributed 974 manually annotated PRs, found 406 PRs (1.7%) exhibited high PR-MCI, and identified eight PR-MCI types, revealing that "descriptions claim unimplemented changes" was the most common issue (45.4%). Statistical tests confirmed that high-MCI PRs had 51.7% lower acceptance rates (28.3% vs. 80.0%) and took 3.5 times longer to merge (55.8 vs. 16.0 hours). Our findings suggest that unreliable PR descriptions undermine trust in AI agents, highlighting the need for PR-MCI verification mechanisms and improved PR generation to enable trustworthy human-AI collaboration. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_04886 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Analyzing Message-Code Inconsistency in AI Coding Agent-Authored Pull Requests Gong, Jingzhi Pinna, Giovanni Bian, Yixin Zhang, Jie M. Software Engineering Artificial Intelligence Pull request (PR) descriptions generated by AI coding agents are the primary channel for communicating code changes to human reviewers. However, the alignment between these messages and the actual changes remains unexplored, raising concerns about the trustworthiness of AI agents. To fill this gap, we analyzed 23,247 agentic PRs across five agents using PR message-code inconsistency (PR-MCI). We contributed 974 manually annotated PRs, found 406 PRs (1.7%) exhibited high PR-MCI, and identified eight PR-MCI types, revealing that "descriptions claim unimplemented changes" was the most common issue (45.4%). Statistical tests confirmed that high-MCI PRs had 51.7% lower acceptance rates (28.3% vs. 80.0%) and took 3.5 times longer to merge (55.8 vs. 16.0 hours). Our findings suggest that unreliable PR descriptions undermine trust in AI agents, highlighting the need for PR-MCI verification mechanisms and improved PR generation to enable trustworthy human-AI collaboration. |
| title | Analyzing Message-Code Inconsistency in AI Coding Agent-Authored Pull Requests |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2601.04886 |