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

• Published in: Heliyon Vol. 5; no. 7; p. e02082
• Main Authors: Rani, Pooja, Mahapatra, G.S.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.07.2019; Elsevier
• Subjects: algorithms; Artificial neural network; Computer science; computer software; Failure prediction; neural networks; NHPP; Particle swarm optimization; Software reliability; Computer science; Failure prediction; Software reliability; Artificial neural network; Particle swarm optimization; NHPP
• ISSN: 2405-8440; 2405-8440
• DOI: 10.1016/j.heliyon.2019.e02082

This paper presents software fault detection, which is dependent upon the effectiveness of the testing and debugging team. A more skilled testing team can achieve higher rates of debugging success, and thereby removing a larger fraction of faults identified without introducing additional faults. A complex software is often subject to two or more stages of testing that exhibits distinct rates of fault discovery. This paper proposes a two-stage Enhanced neighborhood-based particle swarm optimization (NPSO) technique with the assimilation of the three conventional non homogeneous Poisson process (NHPP) based growth models of software reliability by introducing an additional fault introduction parameter. The proposed neuro and swarm recurrent neural network model is compared with similar models, to demonstrate that in some cases the additional fault introduction parameter is appropriate. Both the theoretical and predictive measures of goodness of fit are used for demonstration using data sets through NPSO.