Recommending Auto-completions for Software Modeling Activities

Auto-completion of textual inputs benefits software developers using IDEs. However, graphical modeling tools used to design software do not provide this functionality. The challenges of recommending auto-completions for graphical modeling activities are largely unexplored. Recommending such auto-completions requires detecting meaningful partly completed activities, tolerating variance in user actions, and determining most relevant activities that a user wants to perform. This paper proposes an approach that works in the background while a developer is creating or evolving models 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 activities. We demonstrated the quality of recommendations generated by our approach in a controlled experiment with 16 students evolving models.We recommended 88% of a user's activities within a short list of ten recommendations.