Diff-Based Code Corruption using LLMs for Large-Scale Bugfix Benchmarking
There are various benchmarks to evaluate bugfixing capabilities of Large Language Models. However, most widespread benchmarks do not fully reflect real-world bugfixing practices. They are small, weakening statistical reliability, and the buggy programs are often similar to one another, potentially distorting evaluation results. The range of bug types can also be narrow, failing to capture a representative range of bugs. To address these issues, we introduce MegaBugFix, a large-scale bugfixing benchmark containing 12,629 buggy Python programs synthesized from correct ones by a Large Language Model. Bug injections were generated as diffs representing code changes. Through this approach, we ...
| Authors | Balázs Szalontai, Ábel Szauter, Balázs Márton et al. |
|---|---|
| Primary category | cs.SE |
| Published | 27 June 2026 |
| Last updated at source | 1 July 2026 |
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