SOFTWARE RELIABILITY GROWTH MODELS INCORPORATING CHANGE POINT WITH IMPERFECT FAULT REMOVAL AND ERROR GENERATION

2010 ◽  
Vol 30 (2) ◽  
Author(s):  
V.K. Sehgal ◽  
R. Kapur ◽  
K. Yadav ◽  
D. Kumar
Keyword(s):  
Software Reliability ◽  
Change Point ◽  
Growth Models ◽  
Reliability Growth ◽  
Software Reliability Growth Models ◽  
Software Reliability Growth ◽  
Fault Removal ◽  
Error Generation

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.

Modeling and analysis of software fault detectability and removability with time variant fault exposure ratio, fault removal efficiency, and change point

2018 ◽  
Vol 233 (2) ◽  
pp. 246-256 ◽  
Author(s):  
Subhashis Chatterjee ◽  
Ankur Shukla ◽  
Hoang Pham
Keyword(s):  
Removal Efficiency ◽  
Software Reliability ◽  
Change Point ◽  
Growth Models ◽  
Reliability Growth ◽  
Modeling And Analysis ◽  
Software Reliability Growth ◽  
Software Fault ◽  
Fault Removal ◽  
Exposure Ratio

Software reliability growth models have been proposed to assess and predict the reliability growth of software, remaining number of faults, and failure rate. In previous studies, software faults have been mainly categorized into two categories based on its severity in removal process: simple faults and hard faults. In reality, fault detectability is one of the crucial factors which can influence the reliability growth of software. The detectability of a software fault depends on how frequently the instructions containing faults are executed. However, fault removability of a software fault depends on fault removal efficiency of debugging team. The main motive of this article is to incorporate the fault detectability in software reliability assessment. Fault exposure ratio is an essential factor for software reliability modeling that controls the per-fault hazard rate. It is strongly dependent on fault detectability. In this article, the effect of fault detectability, fault removability, fault exposure ratio, and fault removal efficiency has been considered simultaneously in software reliability growth modeling. Moreover, a logistic fault exposure ratio has been introduced. The effect of change point is incorporated in the proposed software reliability growth model. Two illustrative examples with software testing data have been presented.

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