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

Abstract

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.

Keywords: Artificial neural network; Computer science; Failure prediction; NHPP; Particle swarm optimization; Software reliability.

Figure 1

PNSRNN architecture.

Figure 2

Ring topology of PSO.

Figure 3

Concept for searching standard PSO and pPSO.

Figure 4

Goodness of fit for DS1.

Figure 5

Convergence of fitness values for DS 1.

Figure 6

RPE curve of PNSRNN model for DS 1.

Figure 7

Graph of goodness of fit for DS 2.

Figure 8

Convergence of fitness values for DS 2.

Figure 9

RPE curve of PNSRNN model for DS 2.

Figure 10

Goodness of fit for DS 3.

Figure 11

Convergence of fitness values for DS 3.

Figure 12

RPE curve for DS 3 of PNSRNN model.