Dataset: From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential

From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential Description:This replication package contains all datasets and scripts used in the study From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential. The study investigates the relationships between app features and users' explanation needs, combining correlation analysis and predictive modeling techniques such as logistic regression. The dataset comprises 4,495 user reviews from Google Play Store and Apple App Store, each annotated for explanation needs and enriched with detailed app metadata (e.g., genre, ratings, age restriction, in-app purchases, and more). The original annotaded gold-standard dataset is here: https://doi.org/10.5281/zenodo.11522828 The package includes: Datasets: Annotated app reviews with metadata from the Google Play Store and Apple App Store, as well as validation datasets used to test the predictive models. Correlation Analysis Scripts: Scripts for performing various statistical analyses, including Cramér's V, Pearson, Spearman, and eta-squared correlation tests. Logistic Regression Scripts: Scripts for building and validating logistic regression models to predict explanation needs based on app features. A detailed README file is provided, explaining the folder structure, dataset contents, and the purpose of each script to ensure reproducibility. 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 resource, please cite the following publication: Obaidi, M., Qengaj, K., Droste, J., Deters, H., Herrmann, M., Klünder, J., Schmid, E., Schneider, K. (2025). From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential. 2025 IEEE 33rd International Requirements Engineering Workshop (REW). License Unless otherwise stated, this dataset and all associated resources are provided under the Creative Commons Attribution 4.0 International License (CC BY 4.0). Contact For questions or further information, please contact Martin Obaidi ([e-mail]) or the corresponding authors listed in the publication.