Effort-Index-Based Software Reliability Growth Models and Performance Assessment
COMPSAC '00 24th International Computer Software and Applications Conference
Software Reliability Modeling and Cost Estimation Incorporating Testing-Effort and Efficiency
ISSRE '99 Proceedings of the 10th International Symposium on Software Reliability Engineering
Quantitative Software Reliability Modeling from Testing to Operation
ISSRE '00 Proceedings of the 11th International Symposium on Software Reliability Engineering
Performance analysis of software reliability growth models with testing-effort and change-point
Journal of Systems and Software
Journal of Systems and Software
Analysis of a software reliability growth models: the case of log-logistic test-effort function
MS'06 Proceedings of the 17th IASTED international conference on Modelling and simulation
Considering environmental function in reliability growth modeling from testing to operation
SEA '07 Proceedings of the 11th IASTED International Conference on Software Engineering and Applications
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Numerous stochastic models for the software failure phenomenon based on Nonhomogeneous Poisson Process (NHPP) have been proposed in the recent three decades. Although these models are quite helpful for software developers and have been widely applied in the industrial organizations or research centers, we still need to do more works on examining/estimating the parameters of existing software reliability growth models (SRGMs). In this paper, we investigate and account for three possible trends of software fault detection phenomenon during the testing phase: increasing, decreasing and steady state. We present empirical results from the quantitative studies on evaluating the fault detection process and develop a valid time-variable fault detection rate model which has the inherent flexibility of capturing a wide range of possible fault detection trends. The applicability of the proposed model and the related methods of parametric decomposition are illustrated through several real data sets from different software projects. Our evaluation results show that the analytic parametric decomposition approach for SRGM have a fairly accurate predicting capability. In addition, the testing-effort control problem based on the proposed model is also demonstrated.