Rectifier: Code Translation with Corrector via LLMs

Rectifier: Code Translation with Corrector via LLMs | 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 Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed.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. 24 April 2025 V1 Latest version Share on Rectifier: Code Translation with Corrector via LLMs Authors : Xin Yin , Chao Ni 0000-0002-2906-0598 [email protected] , Tien Nguyen , Shaohua Wang , and Xiaohu Yang Authors Info & Affiliations https://doi.org/10.22541/au.174548366.63392632/v1 260 views 197 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Software migration is garnering increasing attention with the evolution of software and society. Early studies mainly relied on handcrafted translation rules to translate between two languages, the translation process is error-prone and time-consuming. In recent years, researchers have begun to explore the use of pre-trained large language models (LLMs) in code translation. However, code translation is a complex task that LLMs would generate mistakes during code translation, they all produce certain types of errors when performing code translation tasks, which include (1) compilation error, (2) runtime error, (3) functional error, and (4) non-terminating execution. We find that the root causes of these errors are very similar (e.g. failure to import packages, errors in loop boundaries, operator errors, and more). In this paper, we propose a general corrector, namely Rectifier, which is a micro and universal model for repairing translation errors. It learns from errors generated by existing LLMs and can be widely applied to correct errors generated by any LLM. The experimental results on translation tasks between C++, Java, and Python show that our model has effective repair ability, and cross-experiments also demonstrate the robustness of our method. Supplementary Material File (rectifier__code_translation_with_corrector_via_llms.pdf) Download 1.14 MB Information & Authors Information Version history V1 Version 1 24 April 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords code translation large language model repair Authors Affiliations Xin Yin Zhejiang University View all articles by this author Chao Ni 0000-0002-2906-0598 [email protected] Zhejiang University View all articles by this author Tien Nguyen The University of Texas at Dallas View all articles by this author Shaohua Wang Central University of Finance and Economics View all articles by this author Xiaohu Yang Zhejiang University View all articles by this author Metrics & Citations Metrics Article Usage 260 views 197 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Xin Yin, Chao Ni, Tien Nguyen, et al. Rectifier: Code Translation with Corrector via LLMs. Authorea . 24 April 2025. DOI: https://doi.org/10.22541/au.174548366.63392632/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 . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); Cited by Xin Yin, Chao Ni, Xinrui Li, Xiaohu Yang, Improving the ability of pre-trained language model by imparting large language model’s experience, Journal of Systems and Software, 234 , (112744), (2026). https://doi.org/10.1016/j.jss.2025.112744 Crossref Loading... View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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