Turker, U.C., Hierons, R.M. orcid.org/0000-0002-4771-1446, El-Fakih, K. et al. (2 more authors) (2024) Accelerating finite state machine-based testing using reinforcement learning. IEEE Transactions on Software Engineering, 50 (3). pp. 574-597. ISSN 0098-5589
Abstract
Testing is a crucial phase in the development of complex systems, and this has led to interest in automated test generation techniques based on state-based models. Many approaches use models that are types of finite state machine (FSM). Corresponding test generation algorithms typically require that certain test components, such as reset sequences (RSs) and preset distinguishing sequences (PDSs), have been produced for the FSM specification. Unfortunately, the generation of RSs and PDSs is computationally expensive, and this affects the scalability of such FSM-based test generation algorithms. This paper addresses this scalability problem by introducing a reinforcement learning framework: the Q -Graph framework for MBT. We show how this framework can be used in the generation of RSs and PDSs and consider both (potentially partial) timed and untimed models. The proposed approach was evaluated using three types of FSMs: randomly generated FSMs, FSMs from a benchmark, and an FSM of an Engine Status Manager for a printer. In experiments, the proposed approach was much faster and used much less memory than the state-of-the-art methods in computing PDSs and RSs.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 The Authors. Except as otherwise noted, this author-accepted version of a journal article published in IEEE Transactions on Software Engineering is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ |
Keywords: | Finite state machines; reset sequences; state identification sequences; reinforcement learning; Q-value function; software engineering/ software/program verification; software engineering/test design; software engineering/testing and debugging |
Dates: |
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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/R025134/2 Engineering and Physical Sciences Research Council EP/V026801/2 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 29 Jan 2024 13:05 |
Last Modified: | 07 Nov 2024 12:15 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Identification Number: | 10.1109/TSE.2024.3358416 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:207949 |