Evaluating the efficiency of using a search-based automated model merge technique

Abstract

Model-driven engineering relies on effective collaboration between different teams which introduces complex model management challenges. DSE Merge aims to efficiently merge model versions created by various collaborators using search-based exploration of solution candidates that represent conflict-free merged models guided by domain-specific knowledge. In this paper, we report how we systematically evaluated the efficiency of the DSE Merge technique from the user point of view using a reactive experimental Software engineering approach. The empirical tests included the involvement of the intended end users (i.e. engineers), namely undergraduate students, which were expected to confirm the impact of design decisions. In particular, we asked users to merge the different versions of the same model using DSE Merge when compared to using Diff Merge. The experiment showed that to use DSE Merge participant required lower cognitive effort, and expressed their preference and satisfaction with it.