Better Understanding, Better Fixes? A Study of Hallucination in LLM-based Automated Program Repair
Large language models (LLMs) have significantly advanced automated program repair (APR), yet existing evaluations remain largely result-centric and provide limited insight into hallucination during repair. In APR, hallucination may arise not only in final patches but also in the intermediate artifacts that guide patch generation. To address this gap, we perform a multi-layered analysis of hallucination throughout the APR process. Specifically, we characterize hallucination as the production of patches or intermediate artifacts that are not faithfully grounded in the available repair evidence. We examine repair hallucination in final patches and understanding hallucination in intermediate ...
| Authors | Xuemeng Cai, Jiakun Liu, Linhan Yang et al. |
|---|---|
| Primary category | cs.SE |
| Published | 4 September 2026 |
| Last updated at source | 28 September 2026 |
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