Software reliability growth models: A comparison of linear and exponential fault content functions for study of imperfect debugging situations. Issue 1 (1st January 2017)

• Record Type: Journal Article
• Authors: Iqbal, Javaid
• Editors: Pham, Duc
• Is Part Of: Cogent engineering. Volume 4:Issue 1(2017)
• Publication Date: 2017-01-01
• Subjects: software reliability -- reliability growth -- software reliability growth model (SRGM) -- non-homogeneous poisson process (NHPP) -- learning effect -- fault detection rate (FDR) -- imperfect debugging; Engineering -- Periodicals; Technology -- Periodicals; Engineering; Technology; Periodicals; 620
• DOI: 10.1080/23311916.2017.1286739
• Languages: English
• ISSNs: 2331-1916

Abstract

Abstract: The software testing process basically aims at building confidence in the software for its use in real world applications. The reliability of a software system is always important to us. As we carry out the error detection and correction phenomenon on our software, the reliability of the software grows. With an aim to model this growth in the software reliability, many formulations in the form of Software Reliability Growth Models have been proposed. Many of these are based on Non-Homogeneous Poisson Process framework. In this paper, a parallel comparison of the performance of the proposed software reliability growth models is carried out, considering linear and exponential fault content functions for study of imperfect debugging situations. The performance of the proposed models has been compared with some famous existing software reliability models and the proposed models have been validated on some real-world datasets. Three goodness-of-fit criteria that include mean square error, predictive-ratio risk and predictive power are used to carry out the performance comparison of the models. Using these comparison criteria on six actual failure datasets, it is concluded that the proposed Model-2 which always outperforms Model-1, fits the actual failure data better and has better predictive power than other considered SRGMs for at least two data sets.