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This PhD project aims to automatically learn transition systems capturing the behaviour of a whole family of software-based systems. Reasoning at the family level yields important economies of scale and quality improvements for a broad range of systems such as software product lines, adaptive and configurable systems. Yet, to fully benefit from the above advantages, a model of the system family's behaviour is necessary. Such a model is often prohibitively expensive to create manually due to the number of variants. For large long-lived systems with outdated specifications or for systems that continuously adapt, the modelling cost is even higher. Therefore, this PhD proposes to automate the learning of such models from existing artefacts. To advance research at a fundamental level, our learning target are Featured Transition Systems (FTS), an abstract formalism that can be used to provide a pivot semantics to a range of variability-aware state-based modelling languages. The main research questions addressed by this PhD project are: (1) Can we learn variability-aware models efficiently? (2) Can we learn FTS in a black-box fashion? (i.e., with access to execution logs but not to source code); (3) Can we learn FTS in a white/grey-box testing fashion? (i.e., with access to source code); and (4) How do the proposed techniques scale in practice?.

langue originaleAnglais
titreProceedings of the 25th ACM International Systems and Software Product Line Conference, SPLC 2021
Sous-titreDoctoral Symposium
rédacteurs en chefMohammad Mousavi, Pierre-Yves Schobbens, Hugo Araujo, Ina Schaefer, Maurice H. ter Beek, Xavier Devroey, Jose Miguel Rojas, Rick Rabiser, Mahsa Varshosaz, Tomoji Kishi, Jaejoon Lee
EditeurACM Press
Nombre de pages6
ISBN (Electronique)9781450384704
Les DOIs
Etat de la publicationPublié - 6 sept. 2021

Série de publications

NomProceedings of the 25th ACM International Systems and Software Product Line Conference - Volume B

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