The Intersection of Usability Evaluation and Machine Learning in Software Systems
Richard Torres-Molina, Mohammed Seyam · 2023
Usability evaluation is crucial for software systems to promote user goal achievement based on effectiveness, efficiency, and satisfaction. Usability fosters user adoption and retention in software systems. The traditional subjective approach usually utilizes questionnaires, user testing, and heuristics with potential bias embedded. On the other hand, there's limited research on automatic objective evaluation found on usability with ML. Therefore, this paper analyzes the intersection between usability evaluation and ML in software systems to show the current state of the art and potential research directions. In this context, we propose a methodology with ML techniques for usability evaluation on a software system (Moodle) based on user interaction data and quantitative subjective answers. This strategy fosters a predictive analysis with data-driven decision-making for software systems improvement in the long term.