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MultiObjectiveMetamorphicTestCaseSelection_unpublished.pdf (377.7Kb)
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Izenburua
Multi-Objective Metamorphic Test Case Selection: an Industrial Case Study (Practical Experience Report)
Egilea
Ayerdi, JonMondragon Unibertsitatea
Arrieta, AitorMondragon Unibertsitatea
Egilea (beste erakunde batekoa)
Bota Pobee, Ernest
Arratibel, Maite
Ikerketa taldea
Ingeniería del software y sistemas
Argitalpen data
2022
Argitaratzailea
IEEE
Gako-hitzak
Cyber-Physical Systems
Elevators
Metamorphic Testing
Test Selection
Laburpena
Metamorphic testing is a technique that has shown great potential to alleviate the test oracle problem by exploiting the relations among the inputs and outputs of different executions of a system. How ... [+]
Metamorphic testing is a technique that has shown great potential to alleviate the test oracle problem by exploiting the relations among the inputs and outputs of different executions of a system. However, this approach requires multiple test executions. In applications like Cyber-Physical Systems (CPSs), where the test executions can be very expensive in terms of time and resources needed, this can supose a problem. Therefore, it is paramount to optimize the test suite to reduce the costs of verifying the system. Test case selection is an optimization technique which accomplishes this by selecting a subset of test cases while aiming to preserve the effectiveness of the original test suite as much as possible. While there are many approaches for test case selection in the existing literature, none of them has been proposed for the metamorphic test case selection problem, where each metamorphic test case consists of a source and, at least, a follow-up test case pair. In this work, we present an evolutionary multi-objective approach for the metamorphic test case selection problem, adapting existing multi-objective test selection techniques and proposing new evolutionary operators and objective functions. Furthermore, we evaluate our approach with a set of metamorphic tests developed for an industrial case study from the elevation domain. The results suggest that our approach outperforms both Random Search and the same metaheuristic algorithm without the new evolutionary operators we propose. [-]
URI
https://hdl.handle.net/20.500.11984/5888
Argitaratzailearen bertsioa
https://doi.org/10.1109/ISSRE55969.2022.00058
ISBN
978-1-6654-5132-1
Non argitaratua
IEEE 33rd International Symposium on Software Reliability Engineering (ISSRE 2022)  Charlotte, North Carolina 31 October- 3 November 2022.
Dokumentu-mota
Kongresura ekarpena
Bertsioa
Postprinta – Accepted Manuscript
Eskubideak
© 2022 IEEE
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Sarbide bahitua (2025-05-31 arte)
Bildumak
  • Kongresuak - Ingeniaritza [242]

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