Markov Chain Monte Carlo Random Testing

被引：0

作者：

Zhou, Bo ^{[1
]}

Okamura, Hiroyuki ^{[1
]}

Dohi, Tadashi ^{[1
]}

机构：

[1] Hiroshima Univ, Grad Sch Engn, Dept Informat Engn, Higashihiroshima 7398527, Japan

来源：

ADVANCES IN COMPUTER SCIENCE AND INFORMATION TECHNOLOGY, PROCEEDINGS | 2010年 / 6059卷

关键词：

Software testing; Random testing; Bayes statistics; Markov chain Monte Carlo; PROPORTIONAL SAMPLING STRATEGY;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper proposes a software random testing scheme based on Markov chain Monte Carlo (MCMC) method. The significant issue of software testing is how to use the prior knowledge of experienced testers and the information obtained from the preceding test outcomes in making test cases. The concept of Markov chain Monte Carlo random testing (MCMCRT) is based on the Bayes approach to parametric models for software testing, and can utilize the prior knowledge and the information on preceding test outcomes for their parameter estimation. In numerical experiments, we examine effectiveness of MCMCRT with ordinary random testing and adaptive random testing.

引用

页码：447 / 456

页数：10