Change point–based software reliability model under imperfect debugging with revised concept of fault dependency. (December 2016)

• Record Type: Journal Article
• Authors: Chatterjee, Subhashis; Shukla, Ankur
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 230:Number 6(2016:Dec.)
• Page Start: 579
• Page End: 597
• Publication Date: 2016-12
• Subjects: Software reliability -- software reliability growth model -- nonhomogeneous Poisson process -- fault dependency -- imperfect debugging -- change point; Reliability (Engineering) -- Mathematical models -- Periodiclals; Risk assessment -- Mathematical models -- Periodicals; Engineering design -- Mathematical models -- Periodicals; 620.00452
• DOI: 10.1177/1748006X16673767
• Languages: English
• ISSNs: 1748-006X

Abstract

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.