Datasets:
Tasks:
Text Generation
Formats:
parquet
Languages:
English
Size:
10K - 100K
ArXiv:
Tags:
code
DOI:
License:
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README.md
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This is the benchmark for the Bug localization task as part of the
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🏟️ [Long Code Arena benchmark](https://huggingface.co/spaces/JetBrains-Research/long-code-arena).
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The bug localization problem can be formulated as follows: given an issue with a bug description and a repository snapshot in a state where the bug is reproducible, identify the files within the repository that need to be modified to address the reported bug.
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The dataset provides all the required components for evaluation of bug localization approaches in real project-level large-scale data collected from GitHub, including:
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* List of files that should be changed in order to solve the bug;
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* Other additional data and metrics that can be useful in developing new approaches.
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The collected dataset was carefully filtered, enhanced with useful metrics and, what's more, manually labeled, which assures the data quality and provides a golden subset of good examples for evaluation.\
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Moreover, the dataset was split into several categories, namely:
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This is the benchmark for the Bug localization task as part of the
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🏟️ [Long Code Arena benchmark](https://huggingface.co/spaces/JetBrains-Research/long-code-arena).
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The bug localization problem can be formulated as follows: given an issue with a bug description and a repository snapshot in a state where the bug is reproducible, identify the files within the repository that need to be modified to address the reported bug.
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The dataset provides all the required components for evaluation of bug localization approaches in real project-level large-scale data collected from GitHub, including:
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* List of files that should be changed in order to solve the bug;
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* Other additional data and metrics that can be useful in developing new approaches.
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All the repositories are published under permissive licenses (MIT, Apache-2.0, BSD-3-Clause, and BSD-2-Clause). The datapoints can be removed upon request.
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The collected dataset was carefully filtered, enhanced with useful metrics and, what's more, manually labeled, which assures the data quality and provides a golden subset of good examples for evaluation.\
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Moreover, the dataset was split into several categories, namely:
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