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Mining software artefact variants for product line migration and analysis

Jabier Martinez 1
1 MoVe - Modélisation et Vérification
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : Software Product Lines (SPLs) enable the derivation of a family of products based on variability management techniques. Inspired by the manufacturing industry, SPLs use feature configurations to satisfy different customer needs, along with reusable assets to allow systematic reuse. Capitalizing on existing variants by extracting the common and varying elements is referred to as extractive approaches for SPL adoption. Feature identification is needed to analyse the domain variability. Also, to identify the associated implementation elements of the features, their location is needed. In addition, feature constraints should be identified to guarantee that customers are not able to select invalid feature combinations. Then, the reusable assets associated to the features should be constructed. And finally, a comprehensive feature model need to be synthesized. This dissertation presents Bottom-Up Technologies for Reuse (BUT4Reuse), a unified, generic and extensible framework for mining software artefact variants. Special attention is paid to model-driven development scenarios. We also focus on benchmarks and in the analysis of variants, in particular, in benchmarking feature location techniques and in identifying families of variants in the wild for experimenting with feature identification techniques. We present visualisation paradigms to support domain experts on feature naming and to support on feature constraints discovery. Finally, we investigate and discuss the mining of artefact variants for SPL analysis once the SPL is already operational. Concretely, we present an approach to find relevant variants within the SPL configuration space guided by end user assessments.
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Jabier Martinez. Mining software artefact variants for product line migration and analysis. Software Engineering [cs.SE]. Université Pierre et Marie Curie - Paris VI; Université du Luxembourg, 2016. English. ⟨NNT : 2016PA066344⟩. ⟨tel-01477423⟩

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