Risk-based software release policy under parameter uncertainty

Abstract

The determination of the optimal release time is a significant problem in the software development process. Most existing research on this problem is based on the assumption that the model parameters are either known or can be accurately estimated. Due to the uncertainties associated with parameter estimation created by the very limited amount of software failure data that is generally available, the accuracy of the optimum release time determined by traditional approaches is questionable. When the mean value of the optimal release time is used, for example, there is only a 50 per cent chance that the reliability target is met at the time of release. In this paper, an optimal software release policy under parameter uncertainty is studied. To take parameter uncertainty into consideration, an optimal riskbased software release time determination approach is introduced. Application examples are given to illustrate this approach and simulation studies are carried out. The presented results can help management to consider multiple risk levels in order to reach a more reasonable decision.

Modeling and analysis of software fault detectability and removability with time variant fault exposure ratio, fault removal efficiency, and change point

“…The conditional reliability can be obtained using the following equation 33 R(xjt) = e À½m(t + x)Àm (t) ð14Þ…”

After-sales services optimisation through dynamic opportunistic maintenance: a wind energy case study

“…6 Furthermore, there are several uncertainty sources that have to be dealt with during the after-sales services' deployment, such as cost or repair processes, which make it difficult to guarantee a determined service level and a price for providing it. 7,8 In this context, the development of maintenance models plays a key role, since they enable to calculate the quality of the after-sales service (through reliability, availability and maintainability (RAM) analysis), and the cost associated to that service (through life cycle cost (LCC) analysis), allowing a trade-off to be found between the service level to be offered and its price. Moreover, when such maintenance models are developed, the impact of uncertainty sources can also be measured, accurately providing the decision-maker with the necessary information to anticipate the risks to be handled within the after-sales service contracting.…”

“…6 Furthermore, there are several uncertainty sources that have to be dealt with during the after-sales services’ deployment, such as cost or repair processes, which make it difficult to guarantee a determined service level and a price for providing it. 7,8…”

Software reliability growth modeling with dynamic faults and release time optimization using GA and MAUT

“…On the other hand, recently, a few researchers analysed the software reliability using Bayesian approach in imperfect debugging environment to model the dependencies among faults/failures [28,29]. Risk based software release policy, considering the uncertainty in the estimated parameters has been discussed by Xie et al [30] using interval estimation. Similar type of uncertainty has been considered by Pachauri et al [31] and fuzzy set theory is used to minimize the uncertainty in involved estimated parameters.…”