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
收藏
页码：447 / 456
页数：10
相关论文
共 50 条
  • [1] Enhancing Performance of Random Testing through Markov Chain Monte Carlo Methods
    Zhou, Bo
    Okamura, Hiroyuki
    Dohi, Tadashi
    [J]. IEEE TRANSACTIONS ON COMPUTERS, 2013, 62 (01) : 186 - 192
  • [2] Hypothesis testing for Markov chain Monte Carlo
    Benjamin M. Gyori
    Daniel Paulin
    [J]. Statistics and Computing, 2016, 26 : 1281 - 1292
  • [3] Hypothesis testing for Markov chain Monte Carlo
    Gyori, Benjamin M.
    Paulin, Daniel
    [J]. STATISTICS AND COMPUTING, 2016, 26 (06) : 1281 - 1292
  • [4] Application of Markov Chain Monte Carlo Random Testing to Test Case Prioritization in Regression Testing
    Zhou, Bo
    Okamura, Hiroyuki
    Dohi, Tadashi
    [J]. IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, 2012, E95D (09): : 2219 - 2226
  • [5] Estimation of binary Markov random fields using Markov chain Monte Carlo
    Smith, D
    Smith, M
    [J]. JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 2006, 15 (01) : 207 - 227
  • [6] Markov Chain Monte Carlo
    Henry, Ronnie
    [J]. EMERGING INFECTIOUS DISEASES, 2019, 25 (12) : 2298 - 2298
  • [7] Markov Chain Monte Carlo Combined with Deterministic Methods for Markov Random Field Optimization
    Kim, Wonsik
    Lee, Kyoung Mu
    [J]. CVPR: 2009 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, VOLS 1-4, 2009, : 1406 - 1413
  • [8] Computational complexity of Markov chain Monte Carlo methods for finite Markov random fields
    Frigessi, A
    Martinelli, F
    Stander, J
    [J]. BIOMETRIKA, 1997, 84 (01) : 1 - 18
  • [9] Population Markov Chain Monte Carlo
    Laskey, KB
    Myers, JW
    [J]. MACHINE LEARNING, 2003, 50 (1-2) : 175 - 196
  • [10] Monte Carlo integration with Markov chain
    Tan, Zhiqiang
    [J]. JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 2008, 138 (07) : 1967 - 1980
12345