Local features: Enhancing variability modeling in software product lines
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http://hdl.handle.net/2183/36318
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Local features: Enhancing variability modeling in software product linesAuthor(s)
Date
2024-07Citation
de 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.
Abstract
[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.
Keywords
Software product line engineering
Variability specification
Feature models
Web-based geographic information systems
Variability specification
Feature models
Web-based geographic information systems
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Rights
Atribución-NoComercial-SinDerivadas 4.0 Internacional © 2024 The Authors
ISSN
0164-1212
1873-1228
1873-1228