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

TL;DR

This paper introduces a novel method for detecting user-perceived failures in mobile applications by analyzing user traces, focusing on whether users recognize failures, which impacts user experience.

Contribution

It presents an unsupervised detection approach leveraging frontend user traces and app page modeling to identify perceived failures, filling a gap in existing log-based detection methods.

Findings

Effective detection of user-perceived failures demonstrated

High accuracy achieved on real-world user data

Method captures user retry behavior as an indicator

Abstract

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.