Improving Model Inference via W-Set Reduction-Reference-Cited by-同舟云学术

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Improving Model Inference via W-Set Reduction

Published:2022 Issue: Volume: Page:90-105
ISSN:0302-9743
Container-title:Testing Software and Systems
language:
Short-container-title:

Author:

Halm Moritz,Braz Rafael S.,Groz Roland,Oriat Catherine,Simao Adenilso

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-04673-5_7

Reference18 articles.

1. Angluin, D.: Learning regular sets from queries and counterexamples. Inf. Comput. 2, 87–106 (1987)

2. 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, Volume 11026 of Lecture Notes in Computer Science. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-96562-8

3. Bremond, N., Groz, R.: Case studies in learning models and testing without reset. In: 2019 IEEE International Conference on Software Testing, Verification and Validation Workshops, AMOST 2019, ICST Workshops 2019, Xi’an, China, 22 April 2019, pp. 40–45 (2019)

4. Fujiwara, S., von Bochmann, G., Khendek, F., Amalou, M., Ghedamsi, A.: Test selection based on finite state models. IEEE Trans. Softw. Eng. 17(6), 591–603 (1991)

5. Gill, A.: Introduction to the Theory of Finite-State Machines. McGraw-Hill, New York (1962)

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1. Improving Model Learning by Inferring Separating Sequences from Traces;2023 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW);2023-04

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