Türker, U.C., Hierons, R.M. orcid.org/0000-0002-4771-1446, Mousavi, M.R. et al. (1 more author) (2020) Efficient state synchronisation in model-based testing through reinforcement learning. In: 36th IEEE/ACM International Conference on Automated Software Engineering (ASE2021). 36th IEEE/ACM International Conference on Automated Software Engineering (ASE2021), 15-19 Nov 2021, Melbourne, Australia. IEEE (Institute of Electrical and Electronics Engineers) , pp. 368-380. ISBN 978-1-6654-0337-5
Abstract
Model-based testing is a structured method to test complex systems. Scaling up model-based testing to large systems requires improving the efficiency of various steps involved in testcase generation and more importantly, in test-execution. One of the most costly steps of model-based testing is to bring the system to a known state, best achieved through synchronising sequences. A synchronising sequence is an input sequence that brings a given system to a predetermined state regardless of system’s initial state. Depending on the structure, the system might be complete, i.e., all inputs are applicable at every state of the system. However, some systems are partial and in this case not all inputs are usable at every state. Derivation of synchronising sequences from complete or partial systems is a challenging task. In this paper, we introduce a novel Q-learning algorithm that can derive synchronising sequences from systems with complete or partial structures. The proposed algorithm is faster and can process larger systems than the fastest sequential algorithm that derives synchronising sequences from complete systems. Moreover, the proposed method is also faster and can process larger systems than the most recent massively parallel algorithm that derives synchronising sequences from partial systems. Furthermore, the proposed algorithm generates shorter synchronising sequences.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Software testing; Systematics; Learning automata; Memory management; Software algorithms; Test pattern generators; Synchronization |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Funding Information: | Funder Grant number Engineering and Physical Science Research Council EP/V026801/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 01 Sep 2021 08:31 |
Last Modified: | 21 Jun 2023 14:31 |
Status: | Published |
Publisher: | IEEE (Institute of Electrical and Electronics Engineers) |
Refereed: | Yes |
Identification Number: | 10.1109/ASE51524.2021.9678566 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:177333 |