Abstract
In recent years, many intelligent optimization algorithms are applied to the class integration and test order problem and it has been proved that they can efficiently solve this problem. In this paper, the particle swarm optimization algorithm is applied to the class integration and test order problem. First, the initial population (test orders) is generated randomly and each test order is taken as a particle. Here, we map particles to one-dimensional space; then, a particle (an integration test order) can be represented by a position in the one-dimensional space; finally, the optimal particle (integration test order) is selected by particle swarm optimization algorithm. Also, whether the precedence table of dependency relations is introduced as the constraints in evolution can impact the effect of particle swarm optimization approach is validated through experiment. The experimental results show that the particle swarm optimization algorithm is encouraging in solving class integration and test order problem.
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Notes
SOOT. A Java by tecode optimization framework. http://www.sable.mcgill.ca/soot/.
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Funding
This study was funded by NSFC (Grant No. 61502497, 61673384); the Fundamental Research Funds for the Central Universities (2017QNB08); Guangxi Key Laboratory of Trusted Software (kx201609); China Postdoctoral Science Foundation (2015M581887); Science and Technology Program of Xuzhou (KC15SM051).
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Author Yanmei Zhang has received research grants from NSFC (Grant No. 61502497), the Fundamental Research Funds for the Central Universities (Grant No. 2017QNB08) and Guangxi Key Laboratory of Trusted Software (Grant No. kx201609). Author Shujuan Jiang has received research grants from NSFC (Grant No. 61673384). Authors Yanmei Zhang, Shujuan Jiang, Xingya Wang, Ruoyu Chen and Miao Zhang declare that they have no conflict of interest.
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Communicated by V. Loia.
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Zhang, Y., Jiang, S., Wang, X. et al. An optimization algorithm applied to the class integration and test order problem. Soft Comput 23, 4239–4253 (2019). https://doi.org/10.1007/s00500-018-3077-1
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DOI: https://doi.org/10.1007/s00500-018-3077-1