Abstract
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 language | English |
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Journal | CEUR Workshop Proceedings |
Volume | 3378 |
State | Published - 1 Jan 2023 |
Event | Joint of REFSQ-2023 Workshops, Doctoral Symposium, Posters and Tools Track and Journal Early Feedback, REFSQ-JP 2023 - Barcelona, Spain Duration: 17 Apr 2023 → 20 Apr 2023 |
Keywords
- Conceptual Modeling
- Domain Models
- Machine Learning
- Model Derivation
- Requirements Engineering
ASJC Scopus subject areas
- General Computer Science