基于多属性决策和交叉熵优化多维测试覆盖

Optimizing multi-dimensional test coverage based on multiple attribute decision making and cross entropy

随着软件规模和复杂性越来越高,人们对软件质量保证意识也就逐步增强。如何展开测试、优化测试、选择测试方案及提高测试效率成为目前软件工程中面临的现实问题。利用测试覆盖的多维度量指标,提出综合测试覆盖率、测试覆盖率满意度及测试效率作为优化测试方案选择的效用属性,将综合测试覆盖方案应用于马尔可夫测试模型中进行优化,获得了较优的覆盖测试剖面,优化了测试流程。实验结果表明优化的测试覆盖剖面比随机测试能够减少测试用例数,提高缺陷检测效率。

With the scale and complexity of software increasing, people＇s awareness of software quality assurance has gradually strengthened. How to carry out the test, test scheme optimization selection and how to improve the testing efficiency become the reality problems in software engineering. Using the test coverage of the multidimensional metric, it proposes the effectiveness of comprehensive test coverage, satisfaction of test coverage rate and test efficiency as the optimization test scheme selection, uses multiple attribute decision making lifting scheme selection algorithm to measure the optimal test program, ameliorates the severity of the past subjective experience dependent, so as to provide guidance for reasonable, effective and scientific test. The optimization of comprehensive test coverage scheme is applied to the Markov model of software testing. It obtains better coverage test section, optimizes the test process. Experimental results show that the algorithm can reduce the number of test cases required to detect and remove a certain number of software defects in comparison with the random testing strategy.