arXiv:2606.29088v1 Announce Type: cross Abstract: 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 sy
The proliferation of LLMs capable of code generation and bugfixing has led to an increasing need for robust, real-world relevant benchmarks to accurately assess and improve their capabilities, moving past smaller, less diverse datasets.
Accurate and large-scale benchmarking of LLM bugfixing capabilities is crucial for the development of more reliable AI code assistants, directly impacting software development efficiency and quality across industries.
The introduction of MegaBugFix provides a significantly larger and more diverse dataset for evaluating LLM bugfixing, offering a more realistic assessment of their performance compared to previous benchmarks.
- · AI developers
- · Software engineering teams
- · Python developers
- · LLM companies
- · Manual bugfixing processes
- · Small-scale, non-representative code benchmarks
More accurate evaluation of LLM bugfixing leads to faster iteration and improvement of AI-powered coding tools.
Improved AI code assistants reduce development time and costs for software projects, increasing productivity across tech-driven sectors.
Enhanced software reliability due to AI-assisted bugfixing could lead to a systemic increase in the complexity and ambition of software projects, impacting innovation cycles.
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Read at arXiv cs.AI