A testing-domain-dependent software reliability growth model for imperfect debugging environment and its evaluation of goodness-of-fit

2002 ◽  
Vol 86 (1) ◽  
pp. 11-18 ◽  
Author(s):  
Takaji Fujiwara ◽  
Shigeru Yamada
Keyword(s):  
Growth Model ◽  
Software Reliability ◽  
Goodness Of Fit ◽  
Reliability Growth ◽  
Software Reliability Growth Model ◽  
Imperfect Debugging ◽  
Software Reliability Growth

scholarly journals 2-Dimensional Multi-Release Software Reliability Modelling considering Fault Reduction Factor under imperfect debugging

2018 ◽  
Vol 14 (25) ◽  
pp. 1-12 ◽  
Author(s):  
Sameer Anand ◽  
Vibha Verma ◽  
Anu Gupta Aggarwal
Keyword(s):  
Growth Model ◽  
Software Reliability ◽  
Goodness Of Fit ◽  
Reduction Factor ◽  
Release Time ◽  
Reliability Growth ◽  
Software Reliability Growth Model ◽  
Imperfect Debugging ◽  
Proposed Model ◽  
Software Reliability Growth

Introduction: The present research was conducted at the University of Delhi, India in 2017.Methods: We develop a software reliability growth model to assess the reliability of software products released in multiple versions under limited availability of resources and time. The Fault Reduction Factor (frf) is considered to be constant in imperfect debugging environments while the rate of fault removal is given by Delayed S-Shaped model.Results: The proposed model has been validated on a real life four-release dataset by carrying out goodness of fit analysis. Laplace trend analysis was also conducted to judge the trend exhibited by data with respect to change in the system’s reliability.Conclusions: A number of comparison criteria have been calculated to evaluate the performance of the proposed model relative to only time-based multi-release Software Reliability Growth Model (srgm).Originality: In general, the number of faults removed is not the same as the number of failures experienced in given time intervals, so the inclusion of frf in the model makes it better and more realistic. A paradigm shift has been observed in software development from single release to multi release platform.Limitations: The proposed model can be used by software developers to take decisions regarding the release time for different versions, by either minimizing the development cost or maximizing the reliability and determining the warranty policies.

AN NHPP SOFTWARE RELIABILITY GROWTH MODEL WITH IMPERFECT DEBUGGING AND ERROR GENERATION

2014 ◽  
Vol 21 (02) ◽  
pp. 1450008 ◽  
Author(s):  
P. ROY ◽  
G. S. MAHAPATRA ◽  
K. N. DEY
Keyword(s):  
Growth Model ◽  
Software Reliability ◽  
Data Sets ◽  
Reliability Growth ◽  
Software Reliability Growth Model ◽  
Imperfect Debugging ◽  
Failure Data ◽  
Proposed Model ◽  
Software Reliability Growth ◽  
Error Generation

In this paper, we propose a non-homogeneous Poisson process (NHPP) based software reliability growth model (SRGM) in the presence of modified imperfect debugging and fault generation phenomenon. The testing team may not be able to remove a fault perfectly on observation of a failure due to the complexity of software systems and incomplete understanding of software, and the original fault may remain, or get replaced by another fault causing error generation. We have proposed an exponentially increasing fault content function and constant fault detection rate. The total fault content of the software for our proposed model increases rapidly at the beginning of the testing process. It grows gradually at the end of the testing process because of increasing efficiency of the testing team with testing time. We use the maximum likelihood estimation method to estimate the unknown parameters of the proposed model. The applicability of our proposed model and comparisons with established models in terms of goodness of fit and predictive validity have been presented using five known software failure data sets. Experimental results show that the proposed model gives a better fit to the real failure data sets and predicts the future behavior of software development more accurately than the traditional SRGMs.

scholarly journals A Unique Procedure for Accurate Recognition of Software Failures: A Software Reliability Growth Model Based on Special Case of Generalized Gamma Mixture Model

2019 ◽  
Vol 8 (6) ◽  
pp. 1932-1998
Keyword(s):  
Model Selection ◽  
Mixture Model ◽  
Growth Model ◽  
Software Reliability ◽  
Goodness Of Fit ◽  
Reliability Growth ◽  
Software Reliability Growth Model ◽  
Software Failures ◽  
Generalized Gamma ◽  
Software Reliability Growth

This paper pinpoints to detect and eliminate the actual software failures efficiently. The approach fit in a particular case of Generalized Gamma Mixture Model (GGMM), namely Weibull distribution. The approach estimates two parameters using Maximum Likelihood Estimate (MLE). Standard Evaluation metrics like Mean Square Error (MSE), Coefficient of Determination (R2), Sum of Squares (SSE), and Root Means Square Error (RMSE) were calculated. For the justification of the model selection and goodness of fit various model selection frameworks like Chi-Square Goodness of Fit, Wald’s Test, Akaike Information Criteria (AIC), AICc and Schwarz criterion (SBC) were also estimated. The experimentation was carried out on five benchmark datasets which interpret the considered novel technique identifies the actual failures on par with the existing models. This paper presents a novel software reliability growth model which is more effectual in the identification of the failures significantly and help the present software organizations in the release of software free from bugs just in time

Sign in / Sign up

Export Citation Format

Share Document