Automated Extraction of Conceptual Models from User Stories via NLP

Abstract

Natural language (NL) is still the predominant notation that practitioners use to represent software requirements. Albeit easy to read, NL does not readily highlight key concepts and relationships such as dependencies and conflicts. This contrasts with the inherent capability of graphical conceptual models to visualize a given domain in a holistic fashion. In this paper, we propose to automatically derive conceptual models from a concise and widely adopted NL notation for requirements: user stories. Due to their simplicity, we hypothesize that our approach can improve on the low accuracy of previous works. We present an algorithm that combines state-of-the-art heuristics and that is implemented in our Visual Narrator tool. We evaluate our work on two case studies wherein we obtained promising precision and recall results (between 80% and 92%). The creators of the user stories perceived the generated models as a useful artifact to communicate and discuss the requirements, especially for team members who are not yet familiar with the project.