From User Stories to Domain Models: Recommending Relationships between Entities

Maxim Bragilovski, Fabiano Dalpiaz, Arnon Sturm

Research output: Contribution to journalConference articlepeer-review


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.

Original languageAmerican English
JournalCEUR Workshop Proceedings
StatePublished - 1 Jan 2023
EventJoint of REFSQ-2023 Workshops, Doctoral Symposium, Posters and Tools Track and Journal Early Feedback, REFSQ-JP 2023 - Barcelona, Spain
Duration: 17 Apr 202320 Apr 2023


  • Conceptual Modeling
  • Domain Models
  • Machine Learning
  • Model Derivation
  • Requirements Engineering

All Science Journal Classification (ASJC) codes

  • General Computer Science


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