In this paper, we explore the idea of mining unexpected user interactions with mobile apps, as a way to detect usability problems through an unsupervised learning approach. We consider an interaction (or UI event) during task execution as the detection target, and derive novel features from the perspectives of transition probabilities, dwell time distribution, and context-related statistics. We propose an unsupervised learning approach to differentiate between normal and abnormal interactions through clustering on these features. Experimental results validate that the proposed method is effective in detecting different types of abnormal interactions and identifying usability issues.
['Jing Xu', 'Xiang Ding', 'Ke Huang', 'Guanling Chen']
Unsupervised Detection of Abnormal Moments for Usability Testing of Mobile Apps