From Logs to Insights: Exploring User Behavior in RobotStudio - Li, Jinxuan; Tang, Shijia (My University, 2025)
Understanding how users interact with software is essential—not only for designing intuitive interfaces but also for building meaningful, behavior-driven test scenarios. In this study, we explore user behavior in ABB RobotStudio by analyzing a large dataset of backend log files, each recording detailed event traces from real usage sessions. To approach this problem from multiple angles, we employ three methods. First, we apply N-gram analysis to examine what users do, what events tend to occur together, and in what order, providing us with a window into common behavioral patterns. Second, we construct first-order Markov chains to model the likelihood of transitioning from one type of event to another, capturing dynamics user actions. Third, we use clustering and N-gram to investigate whether different types of event sequences naturally emerge. This helps us uncover whether there are distinct features and recurring patterns of usage across clusters. Together, these three methods reveal both structural and sequential aspects of how users interact with RobotStudio. We find that certain behaviors repeat consistently across users and sessions, while others are more context-dependent. These insights offer practical value: they can support user-centered UI improvements and help generate test cases that more closely mirror real-world workflows.
Keywords: log analysis, Longformer, clustering, N-gram, test generation.