Kodezi Chronos is a debugging-first language model that achieves state-of-the-art results on SWE-bench Lite (80.33%) and 67% real-world fix accuracy, over six times better than GPT-4. Built with Adaptive Graph-Guided Retrieval and Persistent Debug Memory. Model available Q1 2026 via Kodezi OS.

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A Systematic Literature Review on Large Language Models for Automated Program Repair

Automatic program repair for Java with generate-and-validate techniques ✌️✌️: jGenProg (2014) - jMutRepair (2016) - jKali (2016) - DeepRepair (2017) - Cardumen (2018) - 3sfix (2018)

[ICML 2020] DrRepair: Learning to Repair Programs from Error Messages

Repilot, a patch generation tool introduced in the ESEC/FSE'23 paper "Copiloting the Copilots: Fusing Large Language Models with Completion Engines for Automated Program Repair"

[ICML 2021] Break-It-Fix-It: Unsupervised Learning for Program Repair

[USENIX Security 25] PatchAgent is a LLM-based practical program repair agent that mimics human expertise.

Automatic program repair system for Java based on dynamic analysis and code synthesis with SMT. Also contains the code of Dynamoth.

Semantic program repair system for C programs

Automatic repair system for static analysis warnings from SonarQube's SonarJava, TDSC 2022 http://arxiv.org/pdf/2103.12033

Automated program repair bibliography, tools and benchmarks.

Research Acceleration Platform that provides interface to multiple state-of-the-art program analysis tools including but not limited to fuzzing, static analysis and program repair.

✅SRepair: Powerful LLM-based Program Repairer with $0.029/Fixed Bug

For our ISSTA20 paper "CoCoNuT: Combining Context-Aware Neural Translation Models using Ensemble for Program Repair" by Thibaud Lutellier, Hung Viet Pham, Lawrence Pang, Yitong Li, Moshi Wei and Lin Tan

For our ICSE23 paper "Impact of Code Language Models on Automated Program Repair" by Nan Jiang, Kevin Liu, Thibaud Lutellier, and Lin Tan

For our ICSE21 paper "CURE: Code-Aware Neural Machine Translation for Automatic Program Repair" by Nan Jiang, Thibaud Lutellier, and Lin Tan

Code of our paper Applying CodeBERT for Automated Program Repair of Java Simple Bugs which is accepted to MSR 2021.

A High-performance, High-extensibility and High-portability APR System

For our ISSTA23 paper "How Effective are Neural Networks for Fixing Security Vulnerabilities?" by Yi Wu, Nan Jiang, Hung Viet Pham, Thibaud Lutellier, Jordan Davis, Lin Tan, Petr Babkin, and Sameena Shah.