1 research outputs found
Explanation Needs in App Reviews: Taxonomy and Automated Detection
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