7 Failure correlation in software reliability models

Published

Journal Article

Perhaps the most stringent restriction in most software reliability models is the assumption of statistical independence among successive software failures. Our research was motivated by the fact that although there are practical situations in which this assumption could be easily violated, much of the published literature on software reliability modeling does not seriously address this issue. The research work in this paper is devoted to developing the software reliability modeling framework that can consider the phenomena of failure correlation and to study its effects on the software reliability measures. The important property of the developed Markov renewal modeling approach is its flexibility. It allows construction of the software reliability model in both discrete time and continuous time, and (depending on the goals) to base the analysis either on Markov chain theory or on renewal process theory. Thus, our modeling approach is an important step toward more consistent and realistic modeling of software reliability. It can be related to existing software reliability growth models. Many input-domain and time-domain models can be derived as special cases under the assumption of failure s-independence. This paper aims at showing that the classical software reliability theory can be extended to consider a sequence of possibly s-dependent software runs, viz, failure correlation. It does not deal with inference nor with predictions,perse. For the model to be fully specified and applied to estimations and predictions in real software development projects, we need to address many research issues, e.g., the • detailed assumptions about the nature of the overall reliability growth, • way modeling-parameters change as a result of the fault-removal attempts. © 2000 IEEE.

Full Text

Duke Authors

Cited Authors

  • Goseva-Popstojanova, K; Trivedi, KS

Published Date

  • December 1, 2000

Published In

Volume / Issue

  • 49 / 1

Start / End Page

  • 37 - 48

International Standard Serial Number (ISSN)

  • 0018-9529

Digital Object Identifier (DOI)

  • 10.1109/24.855535

Citation Source

  • Scopus