Poisson Process Software Reliability Growth Research Articles

A software reliability growth model with Gompertz-logarithmic failure time distribution

PurposeThe Gompertz curve has been used in industry to estimate the number of remaining software faults. This paper aims to introduce a family of distributions for fitting software failure times which subsumes the Gompertz distribution.Design/methodology/approachThe mean value function of the corresponding non-homogenous Poisson process software reliability growth model is presented. Model parameters are estimated by the method of maximum likelihood. A comparison of the new model with eight models that use well-known failure time distributions of exponential, gamma, Rayleigh, Weibull, Gompertz, half normal, log-logistic and lognormal is performed according to the several statistical and informational criteria. Moreover, a Shannon entropy approach is used for ranking and model selection.FindingsNumerical experiments are implemented on five real software failure datasets varying from small to large datasets. The results exhibit that the proposed model is promising and particularly outperforms the Gompertz model in all considered datasets.Originality/valueThe proposed model provides optimized reliability estimation.

Optimal test activity allocation for covariate software reliability and security models

Traditional software reliability growth models enable quantitative assessment of the software testing process by characterizing the fault detection in terms of testing time or effort. However, the majority of these models do not identify specific testing activities underlying fault discovery and thus can only provide limited guidance on how to incrementally allocate effort. Although there are several novel studies focused on covariate software reliability growth models, they are limited to model development, application, and assessment.This paper presents a non-homogeneous Poisson process software reliability growth model incorporating covariates based on the discrete Cox proportional hazards model. An efficient and stable expectation conditional maximization algorithm is applied to identify the model parameters. An optimal test activity allocation problem is formulated to maximize fault discovery. The proposed method is illustrated through numerical examples on two data sets.

A heterogeneous single changepoint software reliability growth model framework

SummaryMany non‐homogeneous Poisson process software reliability growth models (SRGM) are characterized by a single continuous curve. However, failures are driven by factors such as the testing strategy and environment, integration testing and resource allocation, which can introduce one or more changepoint into the fault detection process. Some researchers have proposed non‐homogeneous Poisson process SRGM, but only consider a common failure distribution before and after changepoints. This paper proposes a heterogeneous single changepoint framework for SRGM, which can exhibit different failure distributions before and after the changepoint. Combinations of two simple and distinct curves including an exponential and S‐shaped curve are employed to illustrate the concept. Ten data sets are used to compare these heterogeneous models against their homogeneous counterparts. Experimental results indicate that heterogeneous changepoint models achieve better goodness‐of‐fit measures on 60% and 80% of the data sets with respect to the Akaike information criterion and predictive sum of squares measures.

A Bivariate Autoregressive Software Reliability Model

reliability models play a dominant role in the analysis of failure data for real time command and control software systems. Goel and Okumoto model is a non homogenous Poisson Process software reliability growth model which has gained a lot of importance in software reliability analysis and prediction. The process of parameter estimation is the major drawback of this model because the independent nature of attribute values is considered in estimation. But in real world applications, there are correlations existing among the attributes. Keeping this criterion, a bivariate autoregressive model of order 1 which forms a linear combination of variants namely software faults and test workers is proposed. A numerical illustration is presented to evaluate the performance of the developed model with that of the existing univariate autoregressive models and found that the proposed model outperforms than exiting model in evaluating and predicting software reliability.

Discrete Nonhomogeneous Poisson Process Software Reliability Growth Models Based on Test Coverage

To incorporate the effect of test coverage, we proposed two novel discrete nonhomogeneous Poisson process software reliability growth models in this article using failure data and test coverage, which are both regarding the number of executed test cases instead of execution time. Because one of the most important factors of the coverage‐based software reliability growth models is the test coverage function (TCF), we first discussed a discrete TCF based on beta function. Then we developed two discrete mean value functions (MVF) integrating test coverage and imperfect debugging. Finally, the proposed discrete TCF and MVFs are evaluated and validated on two actual software reliability data sets. The results of numerical illustration demonstrate that the proposed TCF and the MVFs provide better estimation and fitting under comparisons. Copyright © 2012 John Wiley & Sons, Ltd.