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Model independent refusal trace testing

Software Testing is normally one of the main forms of verification and validation used in software development but it is often manual and so expensive and error prone. One of the proposed solutions to this is to use model-based testing, in which testing is based on a model of how the system should b...

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Bibliographic Details
Published in:Science of computer programming 2025-01, Vol.239, p.103173, Article 103173
Main Authors: Gazda, Maciej, Hierons, Robert M.
Format: Article
Language:English
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Summary:Software Testing is normally one of the main forms of verification and validation used in software development but it is often manual and so expensive and error prone. One of the proposed solutions to this is to use model-based testing, in which testing is based on a model of how the system should behave. If the model has a formal semantics, then there is potential to automate systematic test generation. In this paper we consider the case where the semantics of the model is a set of refusal traces, also called failure traces. We show how the notions of fundamental refusal and fundamental refusal trace can be used to derive a normalised transition system, which we call an observation transition system (OTS), from the semantics. We then show how, if this OTS has finitely many states, and we are given a bound m, one can produce a corresponding complete test suite: one that is guaranteed to determine correctness as long as the number of states of the OTS defined by the semantics of the system under test has no more than m states. In practice, the choice of value for m might be based on domain knowledge or a cost-benefit analysis. As far as we are aware, this is the first work to show how a finite complete test suite can be derived when the semantics under consideration is a set of refusal traces. •Finite complete test suite for refusal trace equivalence.•Normalised transition system derived from the semantics.•Test suite reduction using fundamental refusal traces.
ISSN:0167-6423
DOI:10.1016/j.scico.2024.103173