Automated Goal Model Extraction from User Stories Using NLP

Abstract

User stories are commonly used to capture user needs in agile methods due to their ease of learning and understanding. Yet, the simple structure of user stories prevents us from capturing relations among them. Such relations help the developers to better understand and structure the backlog items derived from the user stories. One solution to this problem is to build goal models that provide explicit relations among goals but require time and effort to build. This paper presents a pipeline to automatically generate a goal model from a set of user stories by applying natural language processing (NLP) techniques and our initial heuristics to build realistic goal models. We first parse and identify the dependencies in the user stories, and store the results in a graph database to maintain the relations among the roles, actions, and objects mentioned in the set of user stories. By applying NLP techniques and several heuristics, we generate goal models that resemble human-built models. Automatically generating models significantly decreases the time spent on this tedious task. Our research agenda includes calculating the similarity between the automatically generated models and the expert-built models. Our overarching research goals are to provide i. an NLP-powered framework that generates goal models from a set of user stories, ii. several heuristics to generate goal models that resemble human-built models, and iii. a repository that includes sets of user stories, with corresponding human-built and automatically generated goal models.