A Unified Approach for Developing Software Reliability Growth Models in the Presence of Imperfect Debugging and Error Generation

2011 ◽  
Vol 60 (1) ◽  
pp. 331-340 ◽  
Author(s):  
P. K. Kapur ◽  
H. Pham ◽  
Sameer Anand ◽  
Kalpana Yadav
Keyword(s):  
Software Reliability ◽  
Growth Models ◽  
Reliability Growth ◽  
Unified Approach ◽  
Imperfect Debugging ◽  
Software Reliability Growth Models ◽  
Software Reliability Growth ◽  
Error Generation

An Assessment of Incorporating Log-Logistic Testing Effort Into Imperfect Debugging Delayed S-Shaped Software Reliability Growth Model

2021 ◽  
Vol 9 (3) ◽  
pp. 23-41
Author(s):  
Nesar Ahmad ◽  
Aijaz Ahmad ◽  
Sheikh Umar Farooq
Keyword(s):  
Software Reliability ◽  
Growth Models ◽  
Least Square ◽  
Model Parameters ◽  
Reliability Growth ◽  
Imperfect Debugging ◽  
Testing Effort ◽  
Software Reliability Growth Models ◽  
Software Reliability Growth ◽  
Effort Function

Software reliability growth models (SRGM) are employed to aid us in predicting and estimating reliability in the software development process. Many SRGM proposed in the past claim to be effective over previous models. While some earlier research had raised concern regarding use of delayed S-shaped SRGM, researchers later indicated that the model performs well when appropriate testing-effort function (TEF) is used. This paper proposes and evaluates an approach to incorporate the log-logistic (LL) testing-effort function into delayed S-shaped SRGMs with imperfect debugging based on non-homogeneous Poisson process (NHPP). The model parameters are estimated by weighted least square estimation (WLSE) and maximum likelihood estimation (MLE) methods. The experimental results obtained after applying the model on real data sets and statistical methods for analysis are presented. The results obtained suggest that performance of the proposed model is better than the other existing models. The authors can conclude that the log-logistic TEF is appropriate for incorporating into delayed S-shaped software reliability growth models.

Change point–based software reliability model under imperfect debugging with revised concept of fault dependency

2016 ◽  
Vol 230 (6) ◽  
pp. 579-597 ◽  
Author(s):  
Subhashis Chatterjee ◽  
Ankur Shukla
Keyword(s):  
Software Reliability ◽  
Change Point ◽  
Growth Models ◽  
Reliability Growth ◽  
Imperfect Debugging ◽  
Software Reliability Growth Models ◽  
Software Reliability Growth ◽  
Fault Dependency ◽  
Different Characteristics ◽  
Types Of Faults

A detailed study about the characteristics of different types of faults is necessary to enhance the accuracy of software reliability estimation. Over the last three decades, some software reliability growth models have been proposed considering the possibility of existence of two types of faults in a software: (1) independent and (2) dependent faults. In these software reliability growth models, it is considered that the removal of a leading fault or independent fault causes detection of corresponding dependent faults. In practical, it is noticed that some dependent faults are possible in a software which are removed during the removal of other faults. Moreover, dependent faults may have different characteristics, which cannot be ignored. Considering these facts, a detailed study about the different characteristics of both dependent and independent faults has been performed, and based on this study, dependent faults have been categorized into different categories. Furthermore, a new software reliability growth model has been proposed with revised concept of fault dependency under imperfect debugging by introducing the fault removal proportionality. In addition, the effect of change point on model’s parameters due to different environmental factors has been considered. The fault reduction factor is considered as a proportionality function. Experimental results establish the fact that the performance of the proposed model is better with respect to estimated and predicted cumulative number of faults on some real software failure datasets.

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