Software Reliability Assessment Using Artificial Neural Network Based Flexible Model Incorporating Faults of Different Complexity

Abstract

With growth in demand for zero defects, predicting reliability of software products is gaining importance. Software Reliability Growth Models (SRGM) are used to estimate the reliability of a software product. We have a large number of SRGM; however none of them works across different environments. Recently, Artificial Neural Networks have been applied in software reliability assessment and software reliability growth prediction. In most of the existing research available in the literature, it is considered that similar testing effort is required on each debugging effort. However, in practice, different amount of testing efforts may be required for detection and removal of different type of faults on basis of their complexity. Consequently, faults are classified into three categories on basis of complexity: simple, hard and complex. In this paper we apply neural network methods to build software reliability growth models (SRGM) considering faults of different complexity. Logistic learning function accounting for the expertise gained by the testing team is used for modeling the proposed model. The proposed model assumes that in the simple faults the growth in removal process is uniform whereas, for hard and complex faults, removal process follows logistic growth curve due to the fact that learning of removal team grows as testing progresses. The proposed model has been validated, evaluated and compared with other NHPP model by applying it on two failure/fault removal data sets cited from real software development projects. The results show that 113 Int. J. Rel. Qual. Saf. Eng. 2008.15:113-127. Downloaded from www.worldscientific.com by GEORGE WASHINGTON UNIVERSITY on 02/07/15. For personal use only.the proposed model with logistic function provides improved goodness-of-fit for software failure/fault removal data.

A neuro-particle swarm optimization logistic model fitting algorithm for software reliability analysis

“…Hu et al 25 implemented recurrent neural networks to simultaneously model fault correction and detection. Kapur et al 26 proposed an ANN-based generalized dynamic integrated model that considers learning phenomenon and faults of different complexity. Zheng 27 described an ensemble of ANNs to predict software reliability.…”

Software quality assurance using software reliability growth modelling: state of the art

“…This review brings the existing literature in one place and provides basis for the future research and applications. With this we also discuss some topics of recent interests having huge scope of research (Kapur et al, 2007a(Kapur et al, , 2007e, 2007f, 2008a(Kapur et al, , 2008c(Kapur et al, , 2009e, 2009a(Kapur et al, , 2009b(Kapur et al, , 2009d, future research directions and data analysis aspects.…”

A novel approach of NPSO on dynamic weighted NHPP model for software reliability analysis with additional fault introduction parameter

“…Su and Huang [27] proposed dynamic weighted combinational model (DWCM) based on ANN [60], [61] for prediction of software reliability. Kapur et al ([28], [29]) presented generalized dynamic integrated model using ANN to incorporate learning phenomenon with complexity of faults. Qiuying [30] proposed a testing-coverage software reliability model that not only considers the imperfect debugging, but also the uncertainty of operating environments.…”