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arXiv · 2307.04367

Explanation Needs in App Reviews: Taxonomy and Automated Detection

Max Unterbusch·Mersedeh Sadeghi·Jannik Fischbach·Martin Obaidi·Andreas Vogelsang
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Abstract

Explainability, i.e. the ability of a system to explain its behavior to users, has become an important quality of software-intensive systems. Recent work has focused on methods for generating explanations for various algorithmic paradigms (e.g., machine learning, self-adaptive systems). There is relatively little work on what situations and types of behavior should be explained. There is also a lack of support for eliciting explainability requirements. In this work, we explore the need for explanation expressed by users in app reviews. We manually coded a set of 1,730 app reviews from 8 apps and derived a taxonomy of Explanation Needs. We also explore several approaches to automatically identify Explanation Needs in app reviews. Our best classifier identifies Explanation Needs in 486 unseen reviews of 4 different apps with a weighted F-score of 86%. Our work contributes to a better understanding of users' Explanation Needs. Automated tools can help engineers focus on these needs and ultimately elicit valid Explanation Needs.

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Max Unterbusch, Mersedeh Sadeghi, Jannik Fischbach, Martin Obaidi, Andreas Vogelsang. 2023-07-10. Explanation Needs in App Reviews: Taxonomy and Automated Detection. https://arxiv.org/abs/2307.04367

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