Assessing optimization based strategies for t-way test suite generation: The case for flower-based strategy
Exhaustive testing is extremely difficult to perform owing to the large number of combinations. Thus, sampling and finding the optimal test suite from a set of feasible test cases becomes a central concern. Addressing this issue, the adoption of t-way testing (where t indicates the interaction stren...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2016
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/18261/ http://umpir.ump.edu.my/id/eprint/18261/ http://umpir.ump.edu.my/id/eprint/18261/1/ID77.pdf |
| Summary: | Exhaustive testing is extremely difficult to perform owing to the large number of combinations. Thus, sampling and finding the optimal test suite from a set of feasible test cases becomes a central concern. Addressing this issue, the adoption of t-way testing (where t indicates the interaction strength) has come into the limelight. In order to summarize the achievements so far and facilitate future development, the main focus of this paper is, first, to present a critical comparison of adoption optimization algorithms (OA) as a basis of the t-way test suite generation strategy and, second, to propose a new t-way strategy based on Flower Pollination Algorithm, called Flower Strategy (FS). Analytical and experimental results demonstrate the applicability of FS for t-way test suite generation. |
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