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.
Software Reliability Prediction and Estimation
