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08-10-2019 | Methodologies and Application | Issue 10/2020

Soft Computing 10/2020

Assessing the quality of mobile graphical user interfaces using multi-objective optimization

Journal:
Soft Computing > Issue 10/2020
Authors:
Makram Soui, Mabrouka Chouchane, Mohamed Wiem Mkaouer, Marouane Kessentini, Khaled Ghedira
Important notes
Communicated by V. Loia.

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Abstract

Aesthetic defects are a violation of quality attributes that are symptoms of bad interface design programming decisions. They lead to deteriorating the perceived usability of mobile user interfaces and negatively impact the User’s eXperience (UX) with the mobile app. Most existing studies relied on a subjective evaluation of aesthetic defects depending on end-users feedback, which makes the manual evaluation of mobile user interfaces human-centric, time-consuming, and error-prone. Therefore, recent studies have dedicated their effort to focus on the definition of mathematical formulas that each targets a specific structural quality of the interface. As the UX is tightly dependent on the user profile, the combination and calibration of quality attributes, formulas, and user’s characteristics, when defining a defect, are not straightforward. In this context, we propose a fully automated framework which combines literature quality attributes with the user’s profile to identify aesthetic defects of MUI. More precisely, we consider the mobile user interface evaluation as a multi-objective optimization problem where the goal is to maximize the number of detected violations while minimizing the detection complexity of detection rules and enhancing the interfaces overall quality in means of guidance and coherence coverage. We conducted a comparative study of several evolutionary algorithms in terms of accurately identifying aesthetic defects. We reported their performance in solving the proposed search-based multi-objective optimization problem. The results confirm the efficiency of the indicator-based evolutionary algorithm in terms of assessing the developers in detecting typical defects and also in generating the most accurate detection rules.

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