Adaptive testing for software means that software testing strategy should be adjusted online by using the testing data collected. Existing researches on adaptive testing rely on a simplified controlled Markov chain (CMC) model for software testing, which focus on how to detect and remove all of the defects with minimum expectation cost. The CMC model for software testing employs several unrealistic assumptions and this makes it have limited applicability. In order to overcome the limitations of applicability and low efficiency, this paper presents an improved CMC model with cost constraints by a series of new transformation of limit conditions. This model is designed according to the software cybermetics methodology. It can reach a balance among efficiency, complexity and applicability. Based on this model, a new optimal test strategy of software defects is designed to remove most defects within cost constraints. Then an adaptive testing strategy is proposed which can be adjusted on-line in accordance with the changes of parameters taking place in the controlled object. Finally a set of simulations are conducted and testing results prove the effectiveness of this model.%基于简化的受控Markov链软件自适应测试模型大多是研究如何以最小的期望成本检测并移除所有的缺陷,并在构建模型时对部分条件进行特殊化和理想化处理.针对受控Markov链软件测试模型适用范围小、效率低的缺陷,在软件控制论思想基础上,对制约条件进行了一系列新的转换,提出一种改进的、资源约束的受控Markov链模型,该模型能够在高效性、复杂性和适用性3方面达到一个平衡.根据该模型设计一种新的软件缺陷优化测试策略,再通过参数估计对优化测试策略进行在线调整的方法,以构造软件自适应测试策略.为了证明其有效,利用该模型得到的新的软件自适应测试策略进行仿真实验,进一步得到了有效结果.
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