Title

ARCAMETES: A Learning Approach for Metamorphic Exploration and Testing

RIS ID

146060

Publication Details

Jarman, D., Smith, R., Johnston, O., Towey, D. & Zhou, Z. (2020). ARCAMETES: A Learning Approach for Metamorphic Exploration and Testing. Proceedings - 2020 IEEE/ACM 42nd International Conference on Software Engineering Workshops, ICSEW 2020 (pp. 396-403). New York, United States: Association for Computing Machinery.

Abstract

© 2020 ACM. In its simplest form, software testing consists of creating test cases from a defined input space, running them in the system-under-test (SUT), and evaluating the outputs with a mechanism for determining success or failure (i.e. an oracle). Metamorphic testing (MT) provides powerful concepts for alleviating the problem of a lack of oracles. To increase the adoption of MT among industry practitioners, approaches and tools that lower the effort to identify potential metamorphic relations (MRs) are very much in demand. As such, we propose a learning-based approach to MR discovery and exploration using concepts of metamorphic testing, association rule learning, and combinatorial testing. The results have implications for numerous applications including software testing and program comprehension, among others. These implications set a strong foundation for a future, extensible metamorphic exploration framework.

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Link to publisher version (DOI)

http://dx.doi.org/10.1145/3387940.3391482