Neurocomputing assisted Consensus Based Web-of-Service Software Design Optimization: A Fault-Resilient Reusability Prediction Approach
Keywords:
Web-of-Service Software, Reusability Prediction, Fault-resilience, Ensemble learning, Machine learning, Neurocomputing.Abstract
In this paper, a state-of-art new neuro-computing assisted consensus-based ensemble model was developed for Web-of-Service (WoS) software reusability prediction. In order to achieve higher accuracy with reliability of prediction, the proposed model made enhancement in both data-model as well as classifier-model. More specifically, it applied WSDL-CKJM tool to extract object-oriented-programming (OOP) metrics, which were subsequently processed using univariate logistic regression-based feature extraction followed by sub-sampling method. In the proposed reusability prediction model, to alleviate data or class-imbalance and skewness problem, three different sub-sampling methods were applied including up-sampling, down-sampling and SMOTE sampling. Once obtaining the differently sampled data with the confidence interval of 95%, it was amalgamated together to give rise a composite feature vector pertaining to WMC, CBO, DIT, LCOM, NOC, and RFC OOP-CK metrics, characterizing structural features of the software program. Subsequently, to alleviate computational overhead Wilcoxon Rank Sum Test (WRST) was applied, which retained the most suitable feature set towards reusability prediction. To alleviate the problem of convergence and over-fitting, Min-Max normalization was performed over the selected feature set. Thus, the normalized input features were processed for two-class classification using the proposed neuro-computing assisted homogenous ensemble model. Noticeably, being homogenous ensemble structure, we used ANN variants with gradient descent (GD), radial basis function (RBF), Levenberg Marquardt (LM) and probabilistic neural network (PNN) as base classifiers. The aforesaid base-classifiers helped in estimating the consensus to make each-class classification, where the proposed consensus-based classification model achieved superior accuracy (96.57%), precision (0.94) and recall (0.99), signifying its robustness over the classical standalone classifiers.
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