Detecting User-Perceived Failure in Mobile Applications via Mining User Traces
This addresses a specific gap in mobile app development by focusing on failures users actually notice, though it appears incremental as it builds on existing log-mining methods.
The paper tackles the problem of detecting user-perceived failures in mobile apps, which directly impact user experience, by proposing an approach that uses frontend user traces and an unsupervised algorithm based on backtracking behavior; preliminary evaluation shows good detection performance on real-world data.
Mobile applications (apps) often suffer from failure nowadays. Developers usually pay more attention to the failure that is perceived by users and compromises the user experience. Existing approaches focus on mining large volume logs to detect failure, however, to our best knowledge, there is no approach focusing on detecting whether users have actually perceived failure, which directly influence the user experience. In this paper, we propose a novel approach to detecting user-perceived failure in mobile apps. By leveraging the frontend user traces, our approach first builds an app page model, and applies an unsupervised detection algorithm to detect whether a user has perceived failure. Our insight behind the algorithm is that when user-perceived failure occurs on an app page, the users will backtrack and revisit the certain page to retry. Preliminary evaluation results show that our approach can achieve good detection performance on a dataset collected from real world users.