Dataset: Automatic Generation of Explainability Requirements and Software Explanations From User Reviews

Dataset: Automatic Generation of Explainability Requirements and Software Explanations From User Reviews Authors:Martin Obaidi, Jannik Fischbach, Jakob Droste, Hannah Deters, Marc Herrmann, Jil Klünder, Steffen Krätzig, Hugo Villamizar, and Kurt Schneider This replication package accompanies the study on Automatic Generation of Explainability Requirements and Software Explanations from User Reviews. It includes all datasets and scripts necessary to reproduce the evaluations and analyses presented in the paper. The dataset consists of 58 user reviews with explainability needs, for which explainability requirements and explanations were manually and automatically generated. The package is divided into three key components: Evaluation of Explainability Requirements and Explanations Data from two studies where participants evaluated manually and ChatGPT-generated explainability requirements and explanations. Preference votes and reasoning categories (Clarity, Style, Tone, Correctness, Level of Detail, Relevance) assigned by study participants. Formulation of Explainability Requirements and Explanations Dataset from a workshop with four requirements engineers, who manually formulated explainability requirements and explanations based on user reviews. Tool for Automated Explainability Generation Source code of a UI-based tool that automatically generates summaries, explainability requirements, and explanations from user reviews via ChatGPT API calls. The tool allows manual refinement of generated outputs and supports requirements engineers in analyzing user feedback. A README file is provided, detailing the folder structure, study methodology, and instructions for reproducing the results. This package ensures transparency and enables further research on LLM-supported explainability requirement engineering. This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Grant No.: 470146331, project softXplain (2022–2025). Citation If you use this dataset, please cite the following publication: Obaidi, M., Fischbach, J., Droste, J., Deters, H., Herrmann, M., Klünder, J., Krätzig, S., Villamizar, H., Schneider, K.: Automatic Generation of Explainability Requirements and Software Explanations From User Reviews. 2025 IEEE 33rd International Requirements Engineering Conference Workshops (REW). License This dataset is provided under the Creative Commons Attribution 4.0 International License (CC BY 4.0). Contact For questions regarding the dataset, please contact the corresponding author as listed in the publication.