{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T02:35:48Z","timestamp":1784169348779,"version":"3.55.0"},"reference-count":101,"publisher":"Association for Computing Machinery (ACM)","issue":"7","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62202324"],"award-info":[{"award-number":["62202324"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Softw. Eng. Methodol."],"published-print":{"date-parts":[[2025,9,30]]},"abstract":"<jats:p>\n            Automated Program Repair (APR) has garnered significant attention due to its potential to streamline the bug repair process for human developers. Recently, LLM-based APR methods have shown promise in repairing real-world bugs. However, existing APR methods often utilize patches generated by LLMs without further optimization, resulting in reduced effectiveness due to the lack of program-specific knowledge. Furthermore, the evaluations of these APR methods have typically been conducted under the assumption of perfect fault localization, which may not accurately reflect their real-world effectiveness. To address these limitations, this article introduces an innovative APR approach called G\n            <jats:sc>iant<\/jats:sc>\n            R\n            <jats:sc>epair<\/jats:sc>\n            . Our approach leverages the insight that LLM-generated patches, although not necessarily correct, offer valuable guidance for the patch generation process. Based on this insight, G\n            <jats:sc>iant<\/jats:sc>\n            R\n            <jats:sc>epair<\/jats:sc>\n            first constructs patch skeletons from LLM-generated patches to confine the patch space, and then generates high-quality patches tailored to specific programs through context-aware patch generation by instantiating the skeletons. To evaluate the performance of our approach, we conduct two large-scale experiments. The results demonstrate that G\n            <jats:sc>iant<\/jats:sc>\n            R\n            <jats:sc>epair<\/jats:sc>\n            not only effectively repairs more bugs (an average of 27.78% on Defects4J v1.2 and 23.40% on Defects4J v2.0) than using LLM-generated patches directly, but also outperforms state-of-the-art APR methods by repairing at least 42 and 7 more bugs under perfect and automated fault localization scenarios, respectively.\n          <\/jats:p>","DOI":"10.1145\/3715004","type":"journal-article","created":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T10:25:13Z","timestamp":1737714313000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":19,"title":["Hybrid Automated Program Repair by Combining Large Language Models and Program Analysis"],"prefix":"10.1145","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-7137-3934","authenticated-orcid":false,"given":"Fengjie","family":"Li","sequence":"first","affiliation":[{"name":"College of Intelligence and Computing, Tianjin University, Tianjin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1983-6572","authenticated-orcid":false,"given":"Jiajun","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Intelligence and Computing, Tianjin University, Tianjin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0320-908X","authenticated-orcid":false,"given":"Jiajun","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Intelligence and Computing, Tianjin University, Tianjin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3063-9425","authenticated-orcid":false,"given":"Hongyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Chongqing University, Chongqing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,8,17]]},"reference":[{"issue":"1","key":"e_1_3_1_2_2","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1109\/TSE.2011.104","article-title":"Genprog: A generic method for automatic software repair","volume":"38","author":"Le Goues Claire","year":"2011","unstructured":"Claire Le Goues, ThanhVu Nguyen, Stephanie Forrest, and Westley Weimer. 2011. 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