Unveiling the Power of Code Pre-trained Models in Neural Program Repair: A Systematic Review

Unveiling the Power of Code Pre-trained Models in Neural Program Repair: A Systematic Review | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 26 February 2025 V1 Latest version Share on Unveiling the Power of Code Pre-trained Models in Neural Program Repair: A Systematic Review Authors : Shanggui Zhan , Xingqi Wang 0000-0002-3689-0581 [email protected] , Dan Wei , Xinjian Cao , Junchao Lu , Huizhe Wu , and Yangbo Lin Authors Info & Affiliations https://doi.org/10.22541/au.174058397.77978477/v1 229 views 141 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Automated program repair (APR) aims to automatically fix bugs of software to improve software stability. Recently, Neural Program Repair (NPR) techniques based on Code Pre-trained Models (CodePTMs) have gained significant attention in the APR field. However, no study to date has yet comprehensively explored the effectiveness and possible limitations of CodePTMs for NPR. To fill this gap, this survey provides a systematic review of the current research on CodePTMs-based NPR techniques, highlighting key challenges and proposing future research directions. Overall, our survey aims to provide researchers with a detailed understanding of current approaches and to promote the further development of CodePTMs in the NPR. Supplementary Material File (unveiling the power of code pre-trained models in neural program repair_a systematic review.pdf) Download 349.88 KB Information & Authors Information Version history V1 Version 1 26 February 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords automated program repair deep learning large models pre-training of source code Authors Affiliations Shanggui Zhan Hangzhou Dianzi University School of Computer Science and Technology View all articles by this author Xingqi Wang 0000-0002-3689-0581 [email protected] Hangzhou Dianzi University School of Computer Science and Technology View all articles by this author Dan Wei Hangzhou Dianzi University School of Computer Science and Technology View all articles by this author Xinjian Cao Hangzhou Dianzi University School of Computer Science and Technology View all articles by this author Junchao Lu Hangzhou Dianzi University School of Computer Science and Technology View all articles by this author Huizhe Wu Hangzhou Dianzi University School of Computer Science and Technology View all articles by this author Yangbo Lin Hangzhou Dianzi University School of Computer Science and Technology View all articles by this author Metrics & Citations Metrics Article Usage 229 views 141 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Shanggui Zhan, Xingqi Wang, Dan Wei, et al. Unveiling the Power of Code Pre-trained Models in Neural Program Repair: A Systematic Review. Authorea . 26 February 2025. DOI: https://doi.org/10.22541/au.174058397.77978477/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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