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Learning by sampling: learning behavioral family models from software product lines

  • Published:2021-01 Issue:1 Volume:26 Page:
  • ISSN:1382-3256
  • Container-title:Empirical Software Engineering
  • language:en
  • Short-container-title:Empir Software Eng

Author:

Damasceno Carlos Diego NascimentoORCID,Mousavi Mohammad Reza,Simao Adenilso da Silva

Funder

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Fundação de Amparo à Pesquisa do Estado de São Paulo

University of Leicester

Publisher

Springer Science and Business Media LLC

Subject

Software

Reference138 articles.

1. Aarts F, Heidarian F, Kuppens H, Olsen P, Vaandrager F (2012) Automata learning through counterexample guided abstraction refinement. In: Giannakopoulou D, Méry D (eds) FM 2012: formal methods. https://doi.org/10.1007/978-3-642-32759-9_4. Springer, Berlin, pp 10–27

2. Aichernig BK, Mostowski W, Mousavi MR, Tappler M, Taromirad M (2018) Model learning and model-based testing. In: Bennaceur A, Hähnle R, Meinke K (eds) Machine learning for dynamic software analysis: potentials and limits: International Dagstuhl Seminar 16172, Dagstuhl Castle, Germany, April 24-27, 2016, Revised Papers. https://doi.org/10.1007/978-3-319-96562-8_3. Springer International Publishing, Cham, pp 74–100

3. Al-Hajjaji M, Krieter S, Thüm T, Lochau M, Saake G (2016) Incling: efficient product-line testing using incremental pairwise sampling. In: Proceedings of the 2016 ACM SIGPLAN international conference on generative programming: concepts and experiences, GPCE 2016. https://doi.org/10.1145/2993236.2993253. Association for Computing Machinery, New York, pp 144–155

4. Al-Hajjaji M, Lity S, Lachmann R, Thüm T, Schaefer I, Saake G (2017) Delta-oriented product prioritization for similarity-based product-line testing. In: Proceedings of the 2nd international workshop on variability and complexity in software design, VACE ’17. https://doi.org/10.1109/VACE.2017.8. IEEE Press, pp 34–40

5. Al-Khiaty MAR, Ahmed M (2017) Matching UML class diagrams using a hybridized greedy-genetic algorithm. In: 2017 12th international scientific and technical conference on computer sciences and information technologies (CSIT). https://doi.org/10.1109/STC-CSIT.2017.8098759. IEEE, pp 161–166

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3. Trustworthy Autonomous Systems Through Verifiability;Computer;2023-02

4. Compositional Learning for Interleaving Parallel Automata;Lecture Notes in Computer Science;2023

5. A benchmark for active learning of variability-intensive systems;Proceedings of the 26th ACM International Systems and Software Product Line Conference - Volume A;2022-09-12
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