Guiding Feature Models Synthesis from User-Stories: An Exploratory Approach - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier Access content directly
Conference Papers Year : 2023

Guiding Feature Models Synthesis from User-Stories: An Exploratory Approach

Liam Rice
Mélanie König


User-stories are commonly used to define requirements in agile project management. In Software Product Lines (SPL), a user-story corresponds to a feature description (or part of it), that can be shared by several products. In practice, large SPL include a huge number of user-stories, making variability hard to grasp and handle. In this paper we present an exploratory approach that aims to guide the synthesis of Feature Models that capture and structure the commonalities and the variability expressed in these user-stories. The built Feature Models aim to help the project understanding, maintenance and evolution. Our approach first decomposes the user-stories to extract the roles and the features, using natural language processing techniques. In a second step, we group userstories having the same topics thanks to a clustering method. This contributes to extract more general features. In a third step, we leverage the use of Formal Concept Analysis to extract logical constraints between the features that guide Feature Model synthesis. We illustrate our approach using a dataset from our industrial partner.
Fichier principal
Vignette du fichier
HAL___Guiding_Feature_models_synthesis_from_User_Stories__An_Exploratory_approach.pdf (537.3 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

lirmm-03971078 , version 1 (03-02-2023)



Thomas Georges, Liam Rice, Marianne Huchard, Mélanie König, Clémentine Nebut, et al.. Guiding Feature Models Synthesis from User-Stories: An Exploratory Approach. VaMoS 2023 - 17th International Working Conference on Variability Modelling of Software-Intensive Systems, Jan 2023, Odense, Denmark. pp.65-70, ⟨10.1145/3571788.3571797⟩. ⟨lirmm-03971078⟩
33 View
73 Download



Gmail Facebook X LinkedIn More