SPC based software reliability using Modified Genetic Algorithm: GO model

Abstract

To assess software reliability, many software reliability growth models (SRGMs) have been proposed in the past four decades. In principle, two widely used methods for the parameter estimation of SRGMs are the maximum likelihood estimation (MLE) and the least squares estimation (LSE). However, the approach of these two estimations may impose some restrictions on SRGMs, such as the existence of derivatives from formulated models or the needs for complex calculation. In this paper, a Modified Genetic Algorithm (MGA) with Statistical Process Control (SPC) is proposed to assess the reliability of software considering the Time domain software failure data using Goel-Okumoto (GO) model which is NonHomogenous Poisson Process (NHPP) based. Experiments based on real software failure data are performed, and the results show that the proposed genetic algorithm is more effective and faster than traditional algorithms.