The software reliability models to describe the reliability growth phenomenon are formulated by any stochastic point process with state-dependent or time-dependent intensity function. On the other hand, to deal with the environmental data, which consists of covariates influencing times to software failure, it may be useful to apply the Cox's proportional hazards model for assessing the software reliability. In this paper, we review the proportional hazards software reliability models and discuss the problem to determine the optimal software release time under the expected total software cost criterion. Numerical examples are devoted to examine the dependence of the covariate structure in both the software reliability prediction and the optimal software release decision.
Practical implicationThe proportional hazards software reliability models are enhanced stochastic models to describe the software reliability growth phenomenon. The results obtained in this paper will be useful to design the software testing process in terms of the expected total software cost and to predict the release timing of software products to the market, taking account of the covariate data influencing times to software failure. Also, these results can provide a flexible tool on the decision making for software engineers, software scientists and software managers in the development company.
The determination of the release schedule for a new software product is the most important issue for designing and controlling a software development process. In fact, the optimal software release problem based on some software reliability growth models has been studied by many authors. In this paper, we propose a new method to estimate the optimal software release time under an alternative cost criterion. More precisely, two kinds of artificial neural networks are used to estimate the fault‐detection time observed in both testing and operation phases. As a cost criterion, we adopt the expected cost rate (the expected total software cost per unit testing time). Then, it is shown that the optimization problem to obtain the optimal release time can be reduced to a graphical one to minimize the tangent slope from a point to an (estimated) empirical curve in two‐dimensional space. Through numerical examples using actual fault‐detection time data, it is illustrated that the method proposed is a very useful device to estimate the optimal software release time precisely.
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