Number Of Residual Faults Research Articles

A NHPP Software Reliability Growth Model Considering Learning Process and Number of Residual Faults

A large number of software reliability growth models have been proposed to analyze the reliability of software application during the testing phase. With the increasing demand to deliver high-quality software, more accurate software reliability models are required to estimate the optimal software release time and the cost of testing efforts. In this paper we firstly demonstrate that the G-O model based on NHPP doesn’t need to consider imperfect debugging and new mistakes introduction during the debugging process. Then on the basis of G-O model a flexible and accurate SRGM is proposed, which considers the time-dependent fault detection rate. And in this model the value of fault detection rate is not only calculated with the number of faults remaining in the software system, but also calculated with human’s learning process during testing phase. It is more realistic. Moreover, the experiment results show that the proposed model fits the public failure data better and can provide more accurate software reliability prediction compared with other existing models.

SOFTWARE FAILURE INTENSITY, RELIABILITY AND OPTIMAL STOPPING TIME INCORPORATING REPAIR POLICIES

Reliability of a software application, its failure intensity and the residual number of faults are three important metrics that provide a quantitative assessment of the failure characteristics of an application. Ultimately, it is also necessary, based on these metrics, to determine an optimal release time at which costs justify the stop test decision. Typically, one of the many stochastic models known as software reliability growth models (SRGMs) is used to characterize the failure behavior of an application to provide estimates of the failure intensity, residual number of faults, reliability, and optimal release time and cost. To ensure analytical tractability, SRGMs assume instantaneous repair and thus the estimates of these metrics obtained using SRGMs tend to be optimistic. In practice, repair activity consumes a non trivial amount of time and resources. Also, repair may be conducted according to many policies which reflect the schedule and budget constraints of a project. A few efforts which incorporate repair into SRGMs are restrictive, since they consider only some SRGMs, model the repair process using a constant repair rate, and provide an estimate of only the residual number of faults. These efforts do not address the issue of estimating the failure intensity, reliability and optimal release time and cost in the presence of repair. In this paper we present a generic framework based on the rate-based simulation technique to incorporate repair policies into finite failure non homogeneous Poisson process (NHPP) class of SRGMs. We describe a methodology to compute the failure intensity and reliability in the presence of repair, and apply it to four popular finite failure NHPP models. We also present an economic cost model which considers explicit repair in providing estimates of optimal release time and cost. We illustrate the potential of the framework to quantify the impact of the parameters of the repair policies on the above metrics using examples. Through these examples we discuss how the framework could be used to guide the allocation of resources to achieve the desired reliability target in a cost-effective manner.

Predicting operational software availability and its applications to telecommunication systems

It is essential to predict customer-perceived software availability during software development and determine when to release the software to maintain a balance among time-to-market, development cost and software quality. This paper presents methods and procedures to predict software failure rates from a user perspective in system test phases and to reverse-engineer in order to estimate software release time for given availability targets. Software reliability analysis is conducted based on non-homogenous Poisson process models. Software system test data of current release are used to estimate the number of residual faults by the end of system tests and data of previous releases or similar products (including system test data, post-system test data and field failure data) provide a means to predict a user-perceived average failure rate of a fault. Software system availability can be predicted from these estimates. Both execution and calendar times are considered. A software resource utilization model is developed to transfer one testing time to another. A telecommunications application illustrates how to calculate the failure rate and testing time to meet the software availability requirements.