ENHANCED ALGORITHM OF ARTIFICIAL BEE COLONY (ABC) TO OPTIMIZE MODELS OF SYSTEM RELIABILITY

Mechatronics & Automation Engineering Department, Shanghai University, China ,Samuel Acquah,Li Zhen,Anastasia Krampah-Nkoom,School Of Management And Business Administration, Jiangsu University, China ,

doi:10.21474/ijar01/12362

Mechatronics & Automation Engineering Department, Shanghai University, China , Samuel Acquah + Show 4 more

Open Access

https://doi.org/10.21474/ijar01/12362

Copy DOI

Abstract

In recent times, computer software applications are increasingly becoming an essential basis in several multipurpose domains including medicine, engineering, transportation etc. Consequently, with such wide implementation of software, the imperative need of ensuring certain software quality physiognomies such as efficiency, reliability and stability has ascended. To measure such software quality features, we have to wait until the software is executed, tested and put to use for a certain period of time. Numerous software metrics are presented in this study to circumvent this long and expensive process, and they proved to be awesome method of estimating software reliability models. For this purpose, software reliability prediction models are built. These are used to establish a relationship between internal sub-characteristics such asinheritance, coupling, size, etc. and external software quality attributes such as maintainability, stability, etc. Usingsuchrelationships, one canbuildamodelinordertoestimatethereliabilityofnewsoftware system.Suchmodelsaremainlyconstructedbyeitherstatisticaltechniquessuchasregression,or machine learningtechniquessuchasC4.5andneuralnetworks.The prototype presented isinvigoratedemployingprocedures of machine learninginparticularrule-basedmodels.Thesehaveawhite-boxnaturewhich accordsthecataloguingandmakingthemgood-looktoexpertsinthedomain. In this paper, wesuggest a powerfulinnovative heuristic based on Artificial Bee Colony (ABC) to enhance rule-based software reliability prediction models. The presented approach is authenticated on data describing reliability of classes in an Object-Oriented system. We compare our models to others constructed using other well-established techniques such as C4.5, Genetic Algorithms (GA), Simulated Annealing (SA), Tabu Search (TS), multi-layer perceptron with back-propagation,multi-lay perceptron hybridized with ABC and the majority classifier. Results show that, in most cases, the propose technique out- performs the others in different aspects.

Highlights

It is known that, evaluating the quality, functionality and reliability of a software system has been a major concern in software quality setting
The Stress metric is always added to every group
The metrics are changed to rule sets

Summary

Introduction

It is known that, evaluating the quality, functionality and reliability of a software system has been a major concern in software quality setting. Activation of a fault of an input value leads to an incorrect output [1]. Detection of such an event corresponds to an incidence of a software system failure. The input values to the software modules either internally or externally may be considered as arriving to the software randomly. The software failure may not be generated stochastically, it may be detected in such a manner. It justifies the use of stochastic models of the underlying random process that administers the software failures [2]. Statistical calculations and parameter estimations of software reliability is crucial tool for developing reliable software systems

Methods

Results

Conclusion