Learning-Contextual Variability Models

Paul Temple 1, 2, 3 Mathieu Acher 2, 1, 3 Jean-Marc Jezequel 2, 1, 3 Olivier Barais 2, 1, 3
1 DiverSe - Diversity-centric Software Engineering
Inria Rennes – Bretagne Atlantique , IRISA_D4 - LANGAGE ET GÉNIE LOGICIEL
Abstract : Modeling how contextual factors relate to a software system’s configuration space is usually a manual, error-prone task that depends highly on expert knowledge. Machine-learning techniques can automatically predict the acceptable software configurations for a given context. Such an approach executes and observes a sample of software configurations within a sample of contexts. It then learns what factors of each context will likely discard or activate some of the software’s features. This lets developers and product managers automatically extract the rules that specialize highly configurable systems for specific contexts.
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IEEE Software, Institute of Electrical and Electronics Engineers, 2017, 34 (6), pp.64-70. 〈10.1109/MS.2017.4121211〉
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Soumis le : mercredi 20 décembre 2017 - 16:19:52
Dernière modification le : vendredi 16 novembre 2018 - 01:26:02

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Paul Temple, Mathieu Acher, Jean-Marc Jezequel, Olivier Barais. Learning-Contextual Variability Models. IEEE Software, Institute of Electrical and Electronics Engineers, 2017, 34 (6), pp.64-70. 〈10.1109/MS.2017.4121211〉. 〈hal-01659137〉

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