An adaptive flower pollination algorithm for minimizing software testing redundancy
Optimization is the selection of a best set of parameters from available alternative sets. Global optimization is the task of finding the absolutely best set of parameters. In this paper, we present an adaptive flower pollination algorithm for solving an optimization problem, i.e., minimization of s...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2017
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/21027/ http://umpir.ump.edu.my/id/eprint/21027/ http://umpir.ump.edu.my/id/eprint/21027/1/31.%20An%20adaptive%20flower%20pollination%20algorithm%20for%20minimizing%20software%20testing%20redundancy.pdf |
| Summary: | Optimization is the selection of a best set of parameters from available alternative sets. Global optimization is the task of finding the absolutely best set of parameters. In this paper, we present an adaptive flower pollination algorithm for solving an optimization problem, i.e., minimization of software testing redundancy. In software testing, test engineers often generate a set of test cases to validate against the user requirements to avoid deficiency of the software. A large number of lines of codes cause potential redundancies in software testing. In order to tackle the issue of redundancy, global optimization algorithms are used to systematically minimize the test suite for software testing. We tested the adaptive flower pollination algorithm on a number of experiments in software tests. The results were compared with existing results of some existing algorithms to demonstrate the strength of our algorithm. Comparison shows that our algorithm performs slightly better than the existing algorithms and thus, the proposed algorithm can potentially be used by researchers and test engineers to obtain optimal test suite requiring the minimum time for software testing. |
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