基于缺陷关联度的Markov模型软件优化测试策略

软件测试过程通常期望以最小的成本检测尽可能多的缺陷.为了降低建模复杂度,多数文献通常假设缺陷之间相互独立.但在实际测试中,缺陷之间往往存在关联,并且每个缺陷引发软件失效的严重程度也不相同.充分利用缺陷之间的关联信息,有助于增加相关缺陷的可检测率,提高软件测试效率.因此,提出一种新的思路:利用软件缺陷之间的关联构造缺陷相关系数,引入回扣机制,量化不同严重等级的缺陷所被检测到的价值,综合考虑缺陷相关系数、检测率、回扣三者的权值,以构造基于缺陷关联的最优测试策略.同时,提出复合的优化算法来构造相应的最小生成树,将测试剖面转换成带权的路径问题,以有效地寻找具有最大权值的最优测试路径.另外,改进了已有的剔除策略,以更有效地删除关联缺陷.通过实验仿真,并与其他测试策略相比较,证明了该方法的有效性.

Optimized Software Testing Strategy Based on the Defect Correlation Markov Model

Software testing process normally expects to detect defects as many as possible with minimum cost. In order to reduce the modeling complexity, most works generally assume that all defects are independent of each other. However, in practical testing processes, defects are normally correlated. The software failure severity caused by different defects may also be distinctive. Making full usage of the relationships between correlated defects, it is argued, is beneficial to improve software testing efficiency. This paper proposes a new approach by making usage of the relationship between defects. Firstly, the defects correlation matrix is constructed, and the synthetic balancing weights are designed based on defect correlation coefficient, rebate and detecting rate. Next, the optimal testing problem is converted into a weighted routing problem and a composite optimization algorithm is provided to effectively construct a minimum spanning tree to find an optimal test strategy. Meanwhile, a new defect removing strategy is designed in accordance with the characteristic of the correlated defects to eliminate defects more efficiently. Simulation results show that the proposed approach has higher effectiveness in terms of defect identification rate and system rewards.