Code language models are now trusted collaborators in production workflows for debugging, refactoring, and iterative repair, and every benchmark that evaluates them assumes the instructions they act on are correct. We study what happens when that assumption breaks.
arXiv:2609.10123v1 Announce Type: cross
Abstract: Large language models (LLMs) have become ubiquitous in software development, with LLM-based automated program repair tools increasingly used during c...
By Xietao Wang-Lin, Anton Isopoussu, Louis Mahon
Large language models (LLMs) have become ubiquitous in software development, with LLM-based automated program repair tools increasingly used during code review. In this report, we explore the iterativ...
arXiv:2607. 19843v1 Announce Type: cross Abstract: Large language models (LLMs) have made automated program repair (APR) increasingly practical for real-world bugs, but repairing directly from bug reports remains underconstrained.
By Yuhao Tan, Zhibang Yang, Fangkai Yang, Yuan Yao, Yu Kang, Lu Wang, Pu Zhao, Xin Zhang, Xiaoxing Ma, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang
The paper investigates hallucination in large language model–based automated program repair (APR). It defines hallucination as producing patches or intermediate artifacts that are not grounded in available repair evidence, and analyzes it across final patches and intermediate tasks such as triggering test case identification, line coverage prediction, and additional test case generation. Experiments on 832 Defects4J bugs show that only 21.0%–55.9% of patches pass the developer test suite, with 72.7% of sampled repairs exhibiting hallucinations, often due to incorrect causal localization or repair strategies.
By Xuemeng Cai, Jiakun Liu, Linhan Yang, Wei Ma, Lingxiao Jiang
arXiv:2505. 07372v3 Announce Type: replace-cross Abstract: This paper presents a novel methodology for enhancing Automated Program Repair (APR) through synthetic data generation utilizing Large Language Models (LLMs).
By David de-Fitero-Dominguez, Antonio Garcia-Cabot, Eva Garcia-Lopez