A novel adaptive structure for SOA system effort estimation. Issue 8 (21st June 2016) - Mishra, Siba; Kumar, Chiranjeev
Abstract: This paper addresses a novel adaptive structure to solve the problem of service‐oriented architecture (SOA) system effort estimation. The objective of our work is to combine the insights of signals theory with empirical research to find a recommendation scheme for the problem of SOA system effort estimation. The main motivation for using this structure is to enhance the principle of self‐adaptability to the situation at hand. The proposed structure consists of an adaptive filter composed of linear combiner of filter weights, input and desired values. Additionally, a gradient steepest descent method is used to adjust the filter parameters (training process) using the least mean square algorithm. Furthermore, an experimental analysis is conducted with the proposed structure using the data of 10 past SOA system industrial applications, and in the empirical analysis, two performance measurement metrics and an evaluation function are used to assess the performance in terms of predictive accuracy. The experimental analysis and comparison study are helpful to demonstrate the effectiveness of an estimation technique. The obtained results indicate that an improved predictive accuracy for the problem of SOA system effort estimation has been achieved using the proposed structure when compared with support vector machines, linear, stepwise and ordinary least square regression techniques. Copyright © 2016 John Wiley & Sons, Ltd. Abstract : The proposed work combines the insights of signal theory with empirical research to find a recommendation scheme for the problem of service‐oriented architecture (SOA) system effort estimation. No such methodology exists in the literature so far. This work presents a novel adaptive structure, driven by an adaptive filter for solving the problem of SOA system effort estimation. Experimental results indicate that the improved estimation accuracy of SOA system development effort has been achieved using the proposed adaptive structure for the used dataset