Recommending Auto-Completions for Software Modeling Activities

Abstract. Auto-completion of textual inputs benefits software develop-ers using IDEs and editors. However, graphical modeling tools used to de-sign software do not provide this functionality. The challenges of recom-mending auto-completions for graphical modeling activities are largely unexplored. Recommending auto-completions during modeling requires detecting meaningful partly completed activities, tolerating variance in user actions, and determining the most relevant activity that a user wants to perform. This paper proposes an approach that works in the back-ground while a developer is creating or evolving a model and handles all these challenges. Editing operations are analyzed and matched to a predefined but extensible catalog of common modeling activities for structural UML models. In this paper we solely focus on determining recommendations rather than automatically completing an activity. We demonstrated the quality of recommendations generated by our approach in a controlled experiment with 16 students evolving models. We recom-mended 88 % of the activities that a user wanted to perform within a short list of ten recommendations.