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Title
Multi-Objective Metamorphic Test Case Selection: an Industrial Case Study (Practical Experience Report)
Author
Ayerdi, Jon ccMondragon Unibertsitatea
Arrieta, Aitor ccMondragon Unibertsitatea
Author (from another institution)
Bota Pobee, Ernest
Arratibel, Maite
Research Group
Ingeniería del software y sistemas
Published Date
2022
Publisher
IEEE
Keywords
Cyber-Physical Systems
Elevators
Metamorphic Testing
Test Selection
Abstract
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
Publisher’s version
https://doi.org/10.1109/ISSRE55969.2022.00058
ISBN
978-1-6654-5132-1
Published at
IEEE 33rd International Symposium on Software Reliability Engineering (ISSRE 2022)  Charlotte, North Carolina 31 October- 3 November 2022.
Document type
Conference paper
Version
Postprint – Accepted Manuscript
Rights
© 2022 IEEE
Access
Embargoed Access (until 2025-05-31)
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  • Conferences - Engineering [242]

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