Abstract—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 -independence.
This paper aims at showing that the classical software reliability
theory can be extended to consider a sequence of possibly -dependent
software runs, viz, failure correlation. It does not deal with inference
nor with predictions, per se. 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.
Index Terms—Failure correlation, Markov renewal process, sequence
of dependent software runs, software reliability.
