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

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.

Efficient Software Reliability Prediction With Evolutionary Virtual Data Position Exploration

Determining appropriate software reliability prediction technique is a challenging task for the software development process. So, it is essential for software engineers to develop good quality software product. Though several prediction models are in use for small size data, the estimation of the reliability of software system is crucial. Inadequate data may lead sub-optimal solution. This chapter proposes a technique of increasing training dataset by generating virtual data points original data. For improving the prediction of cumulative failure time in software, multilayer perceptron (MLP)-based virtual data positions (DEVDP) exploration techniques have been proposed. The parameters of the network are optimized by evolutionary algorithm differential evolution (DE). For validation of the model in presence of virtual data point (VDP), eight failure datasets from different sources has been used. The results obtained from the simulation studies indicate that proposed DEVDP exploration technique outperformed traditional models.

Software Reliability Prediction and Estimation

The rapid growth of the software products tends to increase the software application complexity. The complexity affects the software quality which is achieved by means of software reliability. It is desirable to perform reliability analysis at the early phase of Software Development Life Cycle. The paper conducts a thorough analysis on Bayesian model and Markov model which are common for both reliability prediction and estimation. We evaluate the state based model and path based model for reliability assessment and results obtained in both are same.