Early-Stage Prediction of Review Effort in AI-Generated Pull Requests
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866915757288325120 |
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| author | Minh, Dao Sy Duy Kiet, Huynh Trung Quy, Nguyen Lam Phu Hoa, Pham Phu Nguyen, Tran Chi Duong, Nguyen Dinh Ha Tran, Truong Bao |
| author_facet | Minh, Dao Sy Duy Kiet, Huynh Trung Quy, Nguyen Lam Phu Hoa, Pham Phu Nguyen, Tran Chi Duong, Nguyen Dinh Ha Tran, Truong Bao |
| contents | As AI coding agents evolve from autocomplete tools to autonomous "AI workforce" teammates, they introduce a critical new bottleneck: human maintainers must now manage complex interaction loops rather than just reviewing code. Analyzing 33,707 agent-authored PRs, we uncover a stark two-regime reality: agents excel at narrow automation (28.3% of PRs merge instantly), but frequently fail at iterative refinement, leading to "ghosting" (abandonment) when faced with subjective feedback. This creates a hidden "attention tax" on maintainers. We introduce a creation-time Circuit Breaker model to predict high-maintenance PRs before human review begins. By leveraging simple static complexity cues (e.g., file types, patch size), our model identifies the "expensive tail" of contributions with AUC 0.96, enabling a gated triage process. At a 20% review budget, this approach captures 69% of the high-effort PRs, effectively allowing maintainers to fast-fail costly, low-quality agent contributions while fast-tracking simple fixes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_00753 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Early-Stage Prediction of Review Effort in AI-Generated Pull Requests Minh, Dao Sy Duy Kiet, Huynh Trung Quy, Nguyen Lam Phu Hoa, Pham Phu Nguyen, Tran Chi Duong, Nguyen Dinh Ha Tran, Truong Bao Software Engineering D.2.7 As AI coding agents evolve from autocomplete tools to autonomous "AI workforce" teammates, they introduce a critical new bottleneck: human maintainers must now manage complex interaction loops rather than just reviewing code. Analyzing 33,707 agent-authored PRs, we uncover a stark two-regime reality: agents excel at narrow automation (28.3% of PRs merge instantly), but frequently fail at iterative refinement, leading to "ghosting" (abandonment) when faced with subjective feedback. This creates a hidden "attention tax" on maintainers. We introduce a creation-time Circuit Breaker model to predict high-maintenance PRs before human review begins. By leveraging simple static complexity cues (e.g., file types, patch size), our model identifies the "expensive tail" of contributions with AUC 0.96, enabling a gated triage process. At a 20% review budget, this approach captures 69% of the high-effort PRs, effectively allowing maintainers to fast-fail costly, low-quality agent contributions while fast-tracking simple fixes. |
| title | Early-Stage Prediction of Review Effort in AI-Generated Pull Requests |
| topic | Software Engineering D.2.7 |
| url | https://arxiv.org/abs/2601.00753 |