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.从应用功能到解释需求：探析相关性与预测潜力 本复现研究包包含《从应用功能到解释需求：探析相关性与预测潜力》研究所需的全部数据集与脚本代码。本研究聚焦应用功能与用户解释需求之间的关联关系，结合相关性分析与逻辑回归（logistic regression）等预测建模技术展开探究。该数据集包含来自Google Play商店与苹果应用商店（Apple App Store）的4495条用户评论，每条评论均标注了解释需求标签，并补充了详细的应用元数据（如应用类别、评分、年龄限制、应用内购买项目等）。原始标注的金标准数据集可通过以下链接获取：https://doi.org/10.5281/zenodo.11522828 本复现包包含以下内容： 1. 数据集：包含标注了解释需求与元数据的Google Play商店、苹果应用商店用户评论，以及用于验证预测模型的验证数据集。 2. 相关性分析脚本：用于执行各类统计相关性检验的脚本，包括克莱姆V（Cramér's V）、皮尔逊（Pearson）、斯皮尔曼（Spearman）以及eta平方（eta-squared）相关性检验。 3. 逻辑回归脚本：用于构建并验证基于应用功能预测用户解释需求的逻辑回归模型的脚本。 本包附带详细的README文件，对文件夹结构、数据集内容与各脚本的用途进行说明，以确保研究可复现。 本研究由德国研究基金会（Deutsche Forschungsgemeinschaft, DFG）资助，项目编号为470146331，项目名称为softXplain（2022–2025）。 引用 若使用本资源，请引用以下出版物： 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). 许可协议 除非另有说明，本数据集及所有关联资源均采用知识共享署名4.0国际许可协议（Creative Commons Attribution 4.0 International License, CC BY 4.0）进行授权。 联系方式 如有疑问或进一步信息，请联系Martin Obaidi（邮箱：[e-mail]）或论文中列出的通讯作者。