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Computing Transition Probability in Markov Chain for Early Prediction of Software Reliability
Author(s) -
Singh Lalit,
Rajput Hitesh,
Vinod Gopika,
Tripathi A. K.
Publication year - 2016
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.1793
Subject(s) - markov chain , reliability (semiconductor) , markov model , computer science , software quality , reliability engineering , software , computation , markov process , basis (linear algebra) , data mining , algorithm , machine learning , software development , engineering , statistics , mathematics , power (physics) , physics , quantum mechanics , programming language , geometry
Early prediction of software reliability provides basis for evaluating potential reliability during early stages of a project. It also assists in evaluating the feasibility of proposed reliability requirements and provides a rational basis for design and allocation decisions. Many researchers have proposed different approaches to predict the software reliability based on a Markov model. The transition probabilities in between the states of the Markov model are input parameters to predict the software reliability. In the existing approaches, these probabilities are either assumed on some knowledge or computed using analytical method, and hence, it does not give accurate predicted reliability figure. Some authors compute them using operational profile data, but that is possible only after the deployment of the software, and this is not early prediction. The work in this paper is devoted to demonstrate the computation of transition probability in the Markov reliability model taking a case study. The proposed approach has been validated on 47 sets of real data. Copyright © 2015 John Wiley & Sons, Ltd.