A software reliability growth model with Gompertz-logarithmic failure time distribution

Abstract

PurposeThe Gompertz curve has been used in industry to estimate the number of remaining software faults. This paper aims to introduce a family of distributions for fitting software failure times which subsumes the Gompertz distribution.Design/methodology/approachThe mean value function of the corresponding non-homogenous Poisson process software reliability growth model is presented. Model parameters are estimated by the method of maximum likelihood. A comparison of the new model with eight models that use well-known failure time distributions of exponential, gamma, Rayleigh, Weibull, Gompertz, half normal, log-logistic and lognormal is performed according to the several statistical and informational criteria. Moreover, a Shannon entropy approach is used for ranking and model selection.FindingsNumerical experiments are implemented on five real software failure datasets varying from small to large datasets. The results exhibit that the proposed model is promising and particularly outperforms the Gompertz model in all considered datasets.Originality/valueThe proposed model provides optimized reliability estimation.

A novel CRITIC‐TOPSIS approach for optimal selection of software reliability growth model (SRGM)

“…Kumar et al 25 addressed the issues of cost testing, desired level of reliability and software time. Yaghoobi 26 attempts to add a family of distributions subsuming the Gompertz distribution for fitting software failure times and determined mean value function of nonhomogeneous Poisson process software reliability growth model. Anand et al 27 proposed a mathematical model to study the change in reliability due to multiversion software insertion of an infected patch.…”

“…Yang et al 21 Considered random impulsive shocks and statistical analysis method to develop web software reliability model Li et el. 22 Proposed two S-shaped functions describing the growth trend Zhang et al 23 Incorporated rate of mutable fault detection and testing effort function Huang and Kuo 24 Analyzed fault removal process of system software Kumar et al 25 Proposed reliable growth model incorporating software patching Yaghoobi 26 Provided family of Gompertz distribution for fitting software failure times Anand et al 27 Studied the change in reliability due to multi-version software insertion of an infected patch Kumar and Sahni 28 Used optimal control theoretic method to estimate the optimal policy and genetic algorithm for estimating the test effort Liu and Zhao 29 Combined CRITIC and AHP approaches to assess the index weight Bansal et al 30 Designed fuzzy MCDM methods to select software effort estimation model Song and Peng 31 Proposed MCDM method for evaluation of imbalanced classifiers in bankruptcy and credit risk Youssef 32 Incorporated TOPSIS and BWM to rank CSPs Goswami and Mitra 33 Applied ARAS and COPRAS to determine the optimal mobile model from ten alternatives Dahooie et al 34 Used grey additive ratio assessment (ARAS-G) and stepwise weight assessment ratio analysis (SWARA) methods to choose best information technology (IT) Jocic et al 35 Proposed pivot pairwise relative criteria importance assessment (PIPRECIA) method and interval-valued triangular fuzzy additive ratio assessment (ARAS) for the selection of e-learning course Ghenai et al 36 Step-wise Weight Assessment Ratio Analysis/Additive Ratio Assessment (SWARA/ARAS) method for the assessment of sustainability indicators for renewable energy Zavadskas and Turskis 37 ARAS approach to assess microclimate in office rooms Kumar et al 38 Formulated Fuzzy data envelopment analysis (DEA) approach to rank SRGM Sharma et al 39 Developed distance based approach to rank SRGM Kumar et al 40 Developed MCDM based model using entropy and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS)…”