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  This is the data for **Bug Localization** benchmark as part of LCA.
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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 required components for evaluation of **Bug Localization** approaches in real project-level large-scale data collected from GitHub, including:
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  * Bug issue description
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  * Repositories from which the content on the state of the commit SHA where the bug is reproducible can be extracted
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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 approach development
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- The collected dataset was carefully filtered, enhances with useful metrics and, what is more, manually labeled, which assures the data quality and provides a golden subset of good examples for evaluation.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  The following sections will describe the utilities around the dataset as well as detailed dataset content description.
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  ## How-to
 
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  This is the data for **Bug Localization** benchmark as part of LCA.
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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 required components for evaluation of bug localization approaches in real project-level large-scale data collected from GitHub, including:
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  * Bug issue description
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  * Repositories from which the content on the state of the commit SHA where the bug is reproducible can be extracted
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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 approach development
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+ The collected dataset was carefully filtered, enhances with useful metrics and, what is 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 splitted into several categories, naming:
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+
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+ | **Category** | **Description** | **Number of data points** |
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+ |:------------------:|:----------------------------------------:|:----------------------------------------:|
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+ | `py` | Only `.py` files in changes | 4,339 |
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+ | `java` | Only `.java` files in changes | 2,522 |
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+ | 'kt' | Only `.kt` files in changes | 618 |
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+ | 'mixed' | At list one `.java`, `.py`, or `.kt` file in changes with any other changed files | 2,322 |
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+
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+ And splits, naming:
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+ | **Split** | **Description** |
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+ |:------------------:|:----------------------------------------:|
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+ | `dev` | All collected data point |
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+ | `test` | Manually verified data point |
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+ | `train` | Rest data point from `dev` except `test` |
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+
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  The following sections will describe the utilities around the dataset as well as detailed dataset content description.
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  ## How-to