Beyond Accuracy: Behavioral Dynamics of Agentic Multi-Hunk Repair
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914158334705664 |
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| author | Nashid, Noor Ding, Daniel Gallaba, Keheliya Hassan, Ahmed E. Mesbah, Ali |
| author_facet | Nashid, Noor Ding, Daniel Gallaba, Keheliya Hassan, Ahmed E. Mesbah, Ali |
| contents | Automated program repair has traditionally focused on single-hunk defects, overlooking multi-hunk bugs that are prevalent in real-world systems. Repairing these bugs requires coordinated edits across multiple, disjoint code regions, posing substantially greater challenges. We present the first systematic study of LLM-driven coding agents (Claude Code, Codex, Gemini-cli, and Qwen Code) on this task. We evaluate these agents on 372 multi-hunk bugs from the Hunk4J dataset, analyzing 1,488 repair trajectories using fine-grained metrics that capture localization, repair accuracy, regression behavior, and operational dynamics. Results reveal substantial variation: repair accuracy ranges from 25.8% (Qwen Code) to 93.3% (Claude Code) and consistently declines with increasing bug dispersion and complexity. High-performing agents demonstrate superior semantic consistency, achieving positive regression reduction, whereas lower-performing agents often introduce new test failures. Notably, agents do not fail fast; failed repairs consume substantially more resources (39%-343% more tokens) and require longer execution time (43%-427%). Additionally, we developed Maple to provide agents with repository-level context. Empirical results show that Maple improves the repair accuracy of Gemini-cli by 30% through enhanced localization. By analyzing fine-grained metrics and trajectory-level analysis, this study moves beyond accuracy to explain how coding agents localize, reason, and act during multi-hunk repair. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_11012 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Beyond Accuracy: Behavioral Dynamics of Agentic Multi-Hunk Repair Nashid, Noor Ding, Daniel Gallaba, Keheliya Hassan, Ahmed E. Mesbah, Ali Software Engineering Automated program repair has traditionally focused on single-hunk defects, overlooking multi-hunk bugs that are prevalent in real-world systems. Repairing these bugs requires coordinated edits across multiple, disjoint code regions, posing substantially greater challenges. We present the first systematic study of LLM-driven coding agents (Claude Code, Codex, Gemini-cli, and Qwen Code) on this task. We evaluate these agents on 372 multi-hunk bugs from the Hunk4J dataset, analyzing 1,488 repair trajectories using fine-grained metrics that capture localization, repair accuracy, regression behavior, and operational dynamics. Results reveal substantial variation: repair accuracy ranges from 25.8% (Qwen Code) to 93.3% (Claude Code) and consistently declines with increasing bug dispersion and complexity. High-performing agents demonstrate superior semantic consistency, achieving positive regression reduction, whereas lower-performing agents often introduce new test failures. Notably, agents do not fail fast; failed repairs consume substantially more resources (39%-343% more tokens) and require longer execution time (43%-427%). Additionally, we developed Maple to provide agents with repository-level context. Empirical results show that Maple improves the repair accuracy of Gemini-cli by 30% through enhanced localization. By analyzing fine-grained metrics and trajectory-level analysis, this study moves beyond accuracy to explain how coding agents localize, reason, and act during multi-hunk repair. |
| title | Beyond Accuracy: Behavioral Dynamics of Agentic Multi-Hunk Repair |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2511.11012 |