Enhancing Performance of Random Testing through Markov Chain Monte Carlo Methods
Enhancing Performance of Random Testing through Markov Chain Monte Carlo Methods
复制标题
DOI：
10.1109/tc.2011.208
复制
发表时间：
2010-11
影响因子：
3.7
通讯作者：
Bo Zhou;H. Okamura;T. Dohi
中科院分区：
文献类型：
--
作者：
Bo Zhou;H. Okamura;T. Dohi
在本文中，我们提出了一种概率方法来找到故障引起的输入贝叶斯估计的基础上。根据我们对软件测试的概率性认识，利用马尔可夫链蒙特卡罗（MCMC）方法开发了测试用例生成算法。与现有的随机测试方案，如自适应随机测试不同，我们的方法还可以利用软件测试的先验知识。在实验中，我们比较了基于MCMC的随机测试与普通随机测试和自适应随机测试在真实的程序源中的有效性。这些结果表明基于MCMC的随机测试可以大大提高软件测试的有效性。
In this paper, we propose a probabilistic approach to finding failure-causing inputs based on Bayesian estimation. According to our probabilistic insights of software testing, the test case generation algorithms are developed by Markov chain Monte Carlo (MCMC) methods. Dissimilar to existing random testing schemes such as adaptive random testing, our approach can also utilize the prior knowledge on software testing. In experiments, we compare effectiveness of our MCMC-based random testing with both ordinary random testing and adaptive random testing in real program sources. These results indicate the possibility that MCMC-based random testing can drastically improve the effectiveness of software testing.