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Mobile applications (apps) often suffer from failure nowadays. Developersusually pay more attention to the failure that is perceived by users andcompromises the user experience. Existing approaches focus on mining largevolume logs to detect failure, however, to our best knowledge, there is noapproach focusing on detecting whether users have actually perceived failure,which directly influence the user experience. In this paper, we propose a novelapproach to detecting user-perceived failure in mobile apps. By leveraging thefrontend user traces, our approach first builds an app page model, and appliesan unsupervised detection algorithm to detect whether a user has perceivedfailure. Our insight behind the algorithm is that when user-perceived failureoccurs on an app page, the users will backtrack and revisit the certain page toretry. Preliminary evaluation results show that our approach can achieve gooddetection performance on a dataset collected from real world users.

Detecting User-Perceived Failure in Mobile Applications via Mining User Traces