软件可靠性耦合度量的测试用例约简优化

常规软件测试用例中存在冗余用例,会提高软化测试的复杂度,为保证软件可靠性测试的精度,降低运行代价,提出一种基于耦合度量的软件可靠性测试用例约简优化方法。对软件可靠性测试用例展开耦合度量,保留耦合程度高的软件可靠性测试用例,对剩余测试用例展开约简优化,去除冗余用例;在最小用例集原则的基础上,建立软件可靠性测试用例约简优化目标函数,并将蚁群算法引入目标函数的求解过程中,获得约简后的最优软件可靠性测试用例集,完成软件可靠性测试用例的约简优化。实验结果表明,所提方法的约简效果好、运行代价小、丢失率低。

Co-evolutionary cloud-based attribute ensemble multi-agent reduction algorithm

In order to improve the performance of the attribute reduction algorithm to deal with the noisy and uncertain large data, a novel co-evolutionary cloud-based attribute ensemble multi-agent reduction（CCAEMR） algorithm is proposed.First, a co-evolutionary cloud framework is designed under the M apReduce mechanism to divide the entire population into different co-evolutionary subpopulations with a self-adaptive scale. Meanwhile, these subpopulations will share their rewards to accelerate attribute reduction implementation.Secondly, a multi-agent ensemble strategy of co-evolutionary elitist optimization is constructed to ensure that subpopulations can exploit any correlation and interdependency between interacting attribute subsets with reinforcing noise tolerance.Hence, these agents are kept within the stable elitist region to achieve the optimal profit. The experimental results show that the proposed CCAEMR algorithm has better efficiency and feasibility to solve large-scale and uncertain dataset problems with complex noise.