Castro, David deCortiñas, AlejandroRodríguez Luaces, MiguelPedreira, ÓscarSaavedra Places, Ángeles2024-04-232024-04-232024-07de Castro, D., Cortiñas, A., Luaces, M. R., Pedreira, O., & Places, Á. S. (2024). Local features: Enhancing variability modeling in software product lines. Journal of Systems and Software, Vol. 213, 2024, 112035, ISSN 0164-1212, https://doi.org/10.1016/j.jss.2024.112035.0164-12121873-1228http://hdl.handle.net/2183/36318[Abstract]: Context and motivation: Software Product Lines (SPL) enable the creation of software product families with shared core components using feature models to model variability. Choosing features from a feature model to generate a product may not be sufficient in certain situations because the application engineer may need to be able to decide on configuration time the system’s elements to which a certain feature will be applied. Therefore, there is a need to select which features have to be included in the product but also to which of its elements they have to be applied. Objective: We introduce local features that are selectively applied to specific parts of the system during product configuration. Results: We formalize local features using multimodels to establish relationships between local features and other elements of the system models. The paper includes examples illustrating the motivation for local features, a formal definition, and a domain-specific language for specification and implementation. Finally, we present a case study in a real scenario that shows how the concept of local features allowed us to define the variability of a complex system. The examples and the application case show that the proposal achieves higher customization levels at the application engineering phase.engAtribución-NoComercial-SinDerivadas 4.0 Internacional© 2024 The Authorshttp://creativecommons.org/licenses/by-nc-nd/3.0/es/Software product line engineeringVariability specificationFeature modelsWeb-based geographic information systemsjournal articleopen access10.1016/j.jss.2024.112035