通过类搜索算法实现组合测试数据集的全局优化（英文）

发布时间：2018-04-22 11:42

本文选题：Cluster + searching　；参考：《自动化学报》2017年09期

【摘要】：The test suite generation is a key task for combinatorial testing of software. In order to generate high-quality testing data, a cluster searching driven global optimization mechanism is proposed. In this approach, a binary encoding mechanism is used to transform the combination test suite generating problem into a gene sequence optimization problem. Meanwhile, a novel global optimization algorithm, cluster searching algorithm(CSA), is developed to solve it. In this paper, the validity and rationality of problem transformation mechanism is verified, and the details of CSA are shown. The simulations illustrate the proposed mechanism is feasible. Moreover, it is a simpler and more efficient test suite generation approach for small-size combinatorial testing problems.
[Abstract]:The test suite generation is a key task for combinatorial testing of software. In order to generate high-quality testing data, a cluster searching driven global optimization mechanism is proposed. In this approach, a binary encoding mechanism is used to transform the combination test suite generating problem into a gene sequence optimization problem. Meanwhile, a novel global optimization algorithm, cluster searching algorithm(CSA), is developed to solve it. In this paper, the validity and rationality of problem transformation mechanism is verified, and the details of CSA are shown. The simulations illustrate the proposed mechanism is feasible. Moreover, it is a simpler and more efficient test suite generation approach for small-size combinatorial testing problems.
【作者单位】： School
【基金】：supported by the National Natural Science Foundation of China(61203311,61105064) the Scientific Research Program of Shaanxi Provincial Education Department of China(2015JK1672)
【分类号】：TP311.53

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