From User Stories to Domain Models: Recommending Relationships between Entities

User stories are a common notation for expressing requirements, especially in agile development projects.
While user stories provide a detailed account of the functional requirements, they fail to deliver a holistic
view of the domain. As such, they can be complemented with domain models that not only help gain
this comprehensive view, but also serve as a basis for model-driven development. We focus on the
task of recommending relationships between entities in a domain model, assuming that these entities
were previously extracted from a user story collection either manually or through an automated tool.
We investigate whether an approach based on supervised machine learning can recommend essential
relationships in a domain model more accurately than state-of-the-art rule-based methods. Based on
a collection of datasets that we manually labeled and a set of 32 features we engineered, we train a
machine learning model by using a random forest classifier. The results indicate that our approach has
higher precision and F1
-score than the baseline rule-based methods. Our findings provide preliminary
evidence of the suitability of using machine learning to support the development of domain models,
especially in recommending relationships between related entities.