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

共 50 条

[21] Suppressing random walks in Markov chain Monte Carlo using ordered overrelaxation
Neal, RM
[J]. LEARNING IN GRAPHICAL MODELS, 1998, 89 : 205 - 228
[22] An n-dimensional Rosenbrock distribution for Markov chain Monte Carlo testing
Pagani, Filippo
Wiegand, Martin
Nadarajah, Saralees
[J]. SCANDINAVIAN JOURNAL OF STATISTICS, 2022, 49 (02) : 657 - 680
[23] Estimation of Markov random field prior parameters using Markov chain Monte Carlo maximum likelihood
Descombes, X
Morris, RD
Zerubia, J
Berthod, M
[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING, 1999, 8 (07) : 954 - 963
[24] Maximum likelihood estimation of Markov random field parameters using Markov chain Monte Carlo algorithms
Descombes, X
Morris, R
Zerubia, J
Berthod, M
[J]. ENERGY MINIMIZATION METHODS IN COMPUTER VISION AND PATTERN RECOGNITION, PROCEEDINGS, 1997, 1223 : 133 - 148
[25] Sequential Monte Carlo Samplers with Independent Markov Chain Monte Carlo Proposals
South, L. F.
Pettitt, A. N.
Drovandi, C. C.
[J]. BAYESIAN ANALYSIS, 2019, 14 (03): : 753 - 776
[26] On adaptive Markov chain Monte Carlo algorithms
Atchadé, YF
Rosenthal, JS
[J]. BERNOULLI, 2005, 11 (05) : 815 - 828
[27] Estimation via Markov chain Monte Carlo
Spall, JC
[J]. IEEE CONTROL SYSTEMS MAGAZINE, 2003, 23 (02): : 34 - 45
[28] Geometry and Dynamics for Markov Chain Monte Carlo
Barp, Alessandro
Briol, Francois-Xavier
Kennedy, Anthony D.
Girolami, Mark
[J]. ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION, VOL 5, 2018, 5 : 451 - 471
[29] Convergence Diagnostics for Markov Chain Monte Carlo
Roy, Vivekananda
[J]. ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION, VOL 7, 2020, 2020, 7 : 387 - 412
[30] Estimation via Markov chain Monte Carlo
Spall, JC
[J]. PROCEEDINGS OF THE 2002 AMERICAN CONTROL CONFERENCE, VOLS 1-6, 2002, 1-6 : 2559 - 2564

← 1 2 3 4 5 →