Towards Identification of Software Improvements and Specification Updates by Comparing Monitored and Specified End-User Behavior

Support of end-user needs is an important success factor for a software application. In order to optimize the support of end-user needs, developers have to be aware of them and their evolution over time. But a communication gap between developers and users leads to ignorance of developers about how users use their application. Also, developer assumptions about user behavior are rarely tested and corrected if they are wrong. Consequently, many software applications have a mediocre support of user needs and user problems as well as changes in user needs are detected rather late. In this paper, we present a research agenda addressing this problem by comparing use case descriptions to monitored user actions. More specifically, we propose to monitor user actions using instrumentation, detect the current use case of a user using machine learning, and compare use case steps to monitored user actions. By detecting differences between both, we identify mismatches between user behavior and developer assumptions reflected in use case descriptions. Those mismatches can serve as starting points to identify software improvements, to test the use case specification and identify updates, and to revise training programs. Finally, we sketch a plan to evaluate our approach.