An Automatic User Activity Analysis Method for Discovering Latent Requirements: Usability Issue Detection on Mobile Applications

• Published in: Sensors (Basel, Switzerland) Vol. 18; no. 9; p. 2963
• Main Authors: Park, Soojin, Park, Sungyong, Ma, Kyeongwook
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 05.09.2018; MDPI
• Subjects: activity modeling; Adaptation; Applications programs; Architectural engineering; Automation - methods; Computer science; context-awareness; Equipment and Supplies Utilization; International conferences; Internet; Internet of Things; mobile application; Mobile Applications - standards; Mobile Applications - statistics & numerical data; Mobile operating systems; Quality management; Quantitative analysis; Questionnaires; self-adaptive systems; Software engineering; Software quality; usability; Usability testing; User behavior; User interface; United States > US; China; activity modeling; self-adaptive systems; usability; internet of things; context-awareness; mobile application
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s18092963

Starting with the Internet of Things (IoT), new forms of system operation concepts have emerged to provide creative services through collaborations among autonomic devices. Following these paradigmatic changes, the ability of each participating system to automatically diagnose the degree of quality it is providing is inevitable. This paper proposed a method to automatically detect symptoms that hinder certain quality attributes. The method consisted of three steps: (1) extracting information from real usage logs and automatically generating an activity model from the captured information; (2) merging multiple user activity models into a single, representative model; and (3) detecting differences between the representative user activity model, and an expected activity model. The proposed method was implemented in a domain-independent framework, workable on the Android platform. Unlike other related works, we used quantitative evaluation results to show the benefits of applying the proposed method to five Android-based, open-source mobile applications. The evaluation results showed that the average precision rate for the automatic detection of symptoms was 70%, and the success rate for user implementation of usage scenarios demonstrated an improvement of around 21%, when the automatically detected symptoms were resolved.