This contains the replication package of the paper “Synthetic Repo-level Bug Dataset for Training Automated Program Repair Models”.
Source code and data are available in the following links:
Source code: https://anonymous.4open.science/r/SWE-Synth-4401
Survey link: https://survey.swesynth.com
Data: https://huggingface.co/swesynth
The detailed instructions to replicate the experiments and the descriptions of the released data are available in the README.md file in the source code repository.
Automated program repair (APR) aims to autonomously fix software bugs, yet its effectiveness is hampered by the lack of diverse, real-world bug datasets essential for model training. Although combining large-scale mining with human effort can yield such datasets, the associated costs limit scalability. To address this, we introduce a novel, scalable synthetic data pipeline that leverages large language models (LLMs) to generate synthetic bugs through targeted LLM-based code rewriting. Our pipeline is also capable of synthesizing valuable intermediate repair steps and enriches the training signal toward correct fixes. Using our method, we create SWE-Synth, a large and contextually rich dataset of bug-fix pairs that are natural, scalable, automated verifiable, and contain intermediate repair steps. Training LLMs on our synthetic dataset yields context-aware repair strategies, that achieve repair accuracy equivalent to those trained on manually curated datasets from GitHub like SWE-Gym while delivering superior scalability with effortless bug synthesis, as demonstrated on popular benchmarks (SWE-Bench and BugsInPy)