A discrete modelling framework for fault prediction with logistic fault reduction factor. (3rd June 2022)

• Record Type: Journal Article
• Authors: Shrivastava, Avinash K.; Sharma, Ruchi
• Is Part Of: International journal of reliability and safety. Volume 15:Number 3(2021)
• Page Start: 154
• Page End: 169
• Publication Date: 2022-06-03
• Subjects: software reliability -- modelling -- discrete -- prediction -- FRF -- fault reduction factor; Reliability (Engineering) -- Periodicals; Industrial safety -- Periodicals; 620.00452
• Languages: English
• ISSNs: 1479-389X

Abstract

Many software reliability growth models were developed by considering constant and time-dependent (increasing and decreasing) Fault Reduction Factor (FRF). But all these models were developed by considering the continuous-time approach. But sometimes the appropriate unit of fault detection is the number of test runs or the number of test cases executed. Software reliability growth models developed for such cases are termed discrete ones. In this work, we have developed two software reliability growth models with FRF by considering the perfect and imperfect debugging environment. We have further extended our discrete modelling approach for the multi-release of software. FRF is modelled by using logistic distribution. The results obtained for the proposed discrete FRF-based software reliability growth models are compared with the existing discrete models. On comparing the results we see that the proposed models fit better on the three data sets used for numerical illustration.