Nonhomogeneous Error Detection Rate Models for Software Reliability Growth

1984 ◽  
pp. 120-143 ◽  
Author(s):  
Shigeru Yamada ◽  
Shunji Osaki
Keyword(s):  
Software Reliability ◽  
Error Detection ◽  
Detection Rate ◽  
Reliability Growth ◽  
Software Reliability Growth ◽  
Error Detection Rate

scholarly journals Stochastic Differential Equation-Based Flexible Software Reliability Growth Model

2009 ◽  
Vol 2009 ◽  
pp. 1-15 ◽  
Author(s):  
P. K. Kapur ◽  
Sameer Anand ◽  
Shigeru Yamada ◽  
Venkata S. S. Yadavalli
Keyword(s):  
Differential Equation ◽  
Stochastic Differential Equation ◽  
Growth Model ◽  
Software Reliability ◽  
Error Detection ◽  
Reliability Growth ◽  
Software Reliability Growth Model ◽  
Testing Phase ◽  
Continuous State ◽  
Software Reliability Growth

Several software reliability growth models (SRGMs) have been developed by software developers in tracking and measuring the growth of reliability. As the size of software system is large and the number of faults detected during the testing phase becomes large, so the change of the number of faults that are detected and removed through each debugging becomes sufficiently small compared with the initial fault content at the beginning of the testing phase. In such a situation, we can model the software fault detection process as a stochastic process with continuous state space. In this paper, we propose a new software reliability growth model based on Itô type of stochastic differential equation. We consider an SDE-based generalized Erlang model with logistic error detection function. The model is estimated and validated on real-life data sets cited in literature to show its flexibility. The proposed model integrated with the concept of stochastic differential equation performs comparatively better than the existing NHPP-based models.

Software reliability analysis based on a nonhomogeneous error detection rate model

1984 ◽  
Vol 24 (5) ◽  
pp. 915-920 ◽  
Author(s):  
Shigeru Yamada ◽  
Hiroyuki Narihisa ◽  
Hiroshi Ohtera
Keyword(s):  
Reliability Analysis ◽  
Software Reliability ◽  
Error Detection ◽  
Detection Rate ◽  
Rate Model ◽  
Error Detection Rate

A Family of Software Reliability Models with Bathtub-Shaped Fault Detection Rate

2021 ◽  
pp. 2150034
Author(s):  
Maskura Nafreen ◽  
Lance Fiondella
Keyword(s):  
Fault Detection ◽  
Software Reliability ◽  
Detection Rate ◽  
Predictive Accuracy ◽  
Growth Models ◽  
Reliability Growth ◽  
Statistical Measures ◽  
Software Reliability Growth Models ◽  
Classical Models ◽  
Software Reliability Growth

Researchers have proposed several software reliability growth models, many of which possess complex parametric forms. In practice, software reliability growth models should exhibit a balance between predictive accuracy and other statistical measures of goodness of fit, yet past studies have not always performed such balanced assessment. This paper proposes a framework for software reliability growth models possessing a bathtub-shaped fault detection rate and derives stable and efficient expectation conditional maximization algorithms to enable the fitting of these models. The stages of the bathtub are interpreted in the context of the software testing process. The illustrations compare multiple bathtub-shaped and reduced model forms, including classical models with respect to predictive and information theoretic measures. The results indicate that software reliability growth models possessing a bathtub-shaped fault detection rate outperformed classical models on both types of measures. The proposed framework and models may therefore be a practical compromise between model complexity and predictive accuracy.

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