Datasets

Thomas Hirsch, Birgit Hofer: prunedSlicing: Reducing the Length of Dynamic and Relevant Slices by Pruning Boolean Expressions, https://doi.org/10.5281/zenodo.6908074, 2024.
This dataset is also available on GitHub.

Thomas Hirsch, Birgit Hofer: Supplemental Material for Predictive Reranking using Code Smells for Information Retrieval Fault Localization (Version 1.0), https://doi.org/10.5281/zenodo.8186774, 2023.
This dataset is also available on GitHub.

Thomas Hirsch, Birgit Hofer: Supplementary material for ’The MAP metric in Information Retrieval Fault Localization’ (Version 1.0), https://doi.org/10.5281/zenodo.7817016, 2023.

Thomas Hirsch, Birgit Hofer: prunedSlicing – Pruning Boolean Expressions to Shorten Dynamic Slices (Version 1.0), https://doi.org/10.5281/zenodo.6908075, 2022.
This dataset is also available on GitHub.

Thomas Hirsch, Birgit Hofer: fault_type_prediction – Using textual bug reports to predict the fault category of software bugs (Version 1.0), https://doi.org/10.5281/zenodo.6539173, 2022.
This dataset is also available on GitHub.

Thomas Hirsch, Birgit Hofer: artifact_detection – A tool for NLP tasks on textual bug reports. Version 1.0 (2021): https://doi.org/10.5281/zenodo.5519503, Version 2.0 (2022): https://doi.org/10.5281/zenodo.6393129.
This dataset is also available on GitHub.

Thomas Hirsch, Birgit Hofer: Debugging Questionnaire Dataset (Version 1.0), https://doi.org/10.5281/zenodo.4449045, 2021.

Thomas Hirsch, Birgit Hofer: AmadeusGitHubBugDataset (Version 1.0), https://doi.org/10.5281/zenodo.3973048, 2020.
This dataset is also available on GitHub.