{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/dear-a-novel-deep-learning-based-approach-for","title":"DEAR: A Novel Deep Learning-based Approach for Automated Program Repair","arxiv_id":"2205.01859","date":"2022-05-04","proceeding":null,"authors":["Yi Li","Shaohua Wang","Tien N. Nguyen"],"abstract":"The existing deep learning (DL)-based automated program repair (APR) models are limited in fixing general software defects. % We present {\\tool}, a DL-based approach that supports fixing for the general bugs that require dependent changes at once to one or multiple consecutive statements in one or multiple hunks of code. % We first design a novel fault localization (FL) technique for multi-hunk, multi-statement fixes that combines traditional spectrum-based (SB) FL with deep learning and data-flow analysis. It takes the buggy statements returned by the SBFL model, detects the buggy hunks to be fixed at once, and expands a buggy statement $s$ in a hunk to include other suspicious statements around $s$. We design a two-tier, tree-based LSTM model that incorporates cycle training and uses a divide-and-conquer strategy to learn proper code transformations for fixing multiple statements in the suitable fixing context consisting of surrounding subtrees. We conducted several experiments to evaluate {\\tool} on three datasets: Defects4J (395 bugs), BigFix (+26k bugs), and CPatMiner (+44k bugs). On Defects4J dataset, {\\tool} outperforms the baselines from 42\\%--683\\% in terms of the number of auto-fixed bugs with only the top-1 patches. On BigFix dataset, it fixes 31--145 more bugs than existing DL-based APR models with the top-1 patches. On CPatMiner dataset, among 667 fixed bugs, there are 169 (25.3\\%) multi-hunk/multi-statement bugs. {\\tool} fixes 71 and 164 more bugs, including 52 and 61 more multi-hunk/multi-statement bugs, than the state-of-the-art, DL-based APR models.","url_abs":"https://arxiv.org/abs/2205.01859v1","url_pdf":"https://arxiv.org/pdf/2205.01859v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"dear-a-novel-deep-learning-based-approach-for","repo_url":"https://github.com/AutomatedProgramRepair-2021/dear-auto-fix","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}