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license: mit |
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--- |
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## Dataset Description |
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- **Repository:** [MORepair](https://github.com/buaabarty/morepair) |
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- **Paper:** [MORepair: Teaching LLMs to Repair Code via Multi-Objective Fine-tuning](https://arxiv.org/abs/2404.12636) |
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- **Point of Contact:** [Boyang Yang](mailto:yby@ieee.org) |
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### Dataset Summary |
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EvalRepair-Java is a benchmark for evaluating Java program repair performance, derived from HumanEval. It contains 163 single-function repair tasks, each with a buggy implementation and its corresponding fixed version. |
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### Supported Tasks |
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- Program Repair: Fixing bugs in Java functions |
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- Code Generation: Generating correct implementations from buggy code |
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### Dataset Structure |
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Each row contains: |
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- `task_id`: Unique identifier for the task (same as HumanEval) |
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- `buggy_code`: The buggy implementation |
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- `fixed_code`: The correct implementation |
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- `unit_test`: Unit tests for verifying the correctness of the implementation |
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### Source Data |
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This dataset is derived from HumanEval, a benchmark for evaluating code generation capabilities. We manually introduced bugs into the original implementations and verified the fixes. |
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### Citation |
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```bibtex |
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@article{morepair, |
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author = {Yang, Boyang and Tian, Haoye and Ren, Jiadong and Zhang, Hongyu and Klein, Jacques and Bissyande, Tegawende and Le Goues, Claire and Jin, Shunfu}, |
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title = {MORepair: Teaching LLMs to Repair Code via Multi-Objective Fine-Tuning}, |
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year = {2025}, |
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publisher = {Association for Computing Machinery}, |
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issn = {1049-331X}, |
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url = {https://doi.org/10.1145/3735129}, |
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doi = {10.1145/3735129}, |
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journal = {ACM Trans. Softw. Eng. Methodol.}, |
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} |
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``` |
