Prediction of quality using ANN based on Teaching‐Learning Optimization in component‐based software systems. (25th January 2018)

• Record Type: Journal Article
• Authors: Tomar, Pradeep; Mishra, Rajesh; Sheoran, Kavita
• Is Part Of: Software, practice & experience. Volume 48:Number 4(2018)
• Page Start: 896
• Page End: 910
• Publication Date: 2018-01-25
• Subjects: artificial neural network -- bounded interface complexity metric -- interface surface consistency -- self‐completeness of component's parameter -- self‐completeness of component's return value -- Teaching‐Learning–based Optimization; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.2562
• Languages: English
• ISSNs: 0038-0644

Abstract

Summary: The primary objective of our research work is to enhance the prediction of the quality of a component‐based software system and to develop an artificial neural network (ANN) model for the system reliability optimization problem. In this paper, we introduced the ANN‐supported Teaching‐Learning Optimization by transforming constraints to objective functions. Artificial neural network techniques are found to be powerful in the modeling software package quality metrics compared with the ancient statistical techniques. Therefore, by using the neural network, the quality characteristics of software components of the proposed work are predicted. A nonlinear differentiable transfer function of ANN used in the proposed approach is hyperbolic tangent sigmoid. A new efficient optimization methodology referred to as the Teaching‐Learning–based Optimization is proposed in this paper to optimize reliability and different cost functions. The weight values of the network are then adjusted consistent with a proposed optimization rule, therefore minimizing the network error. The proposed work is implemented in MATLAB by using the Neural Network Toolbox. The proposed work provides improved performance in terms of sensitivity, precision, specificity, negative predictive value, fall‐out or false positive rate, false discovery rate, accuracy, Matthews correlation coefficient, and rate of convergence.