Novel Strategy Generating Variable-Length State Machine Test Paths
2022 (English) In: International journal of software engineering and knowledge engineering, ISSN 0218-1940, Vol. 32, no 08, p. 1247-1278Article in journal (Refereed) Published
Abstract [en]
Finite State Machine is a popular modeling notation for various systems, especially software and electronic. Test paths (TPs) can be automatically generated from the system model to test such systems using a suitable algorithm. This paper presents a strategy that generates TPs and allows to start and end TPs only in defined states of the finite state machine. The strategy also simultaneously supports generating TPs only of length in a given range. For this purpose, alternative system models, test coverage criteria, and a set of algorithms are developed. The strategy is compared with the best alternative based on the reduction of the test set generated by the established N-switch coverage approach on a mix of 171 industrial and artificially generated problem instances. The proposed strategy outperforms the compared variant in a smaller number of TP steps. The extent varies with the used test coverage criterion and preferred TP length range from none to two and half fold difference. Moreover, the proposed technique detected up to 30% more simple artificial defects inserted into experimental SUT models per one test step than the compared alternative technique. The proposed strategy is well applicable in situations where a possible TP starts and ends in a state machine needs to be reflected and, concurrently, the length of the TPs has to be in a defined range.
Place, publisher, year, edition, pages World Scientific, 2022. Vol. 32, no 08, p. 1247-1278
Keywords [en]
Finite automata, Internet of things, Model checking, Finite states machine, Model based testing, Novel strategies, Path-based, Path-based testing, Software testings, State-machine, System models, System testing, Test coverage criteria, Software testing
National Category
Computer Sciences Software Engineering Computer Systems
Research subject Computer Science
Identifiers URN: urn:nbn:se:kau:diva-91823 DOI: 10.1142/S0218194022500474 ISI: 000848724700001 Scopus ID: 2-s2.0-85136515598 OAI: oai:DiVA.org:kau-91823 DiVA, id: diva2:1694132
Funder Knowledge Foundation, 20200067 2022-09-082022-09-082022-10-31 Bibliographically approved