An advanced mechanism for Software Size Estimation using combinational artificial intelligence

Abstract

Software Size Estimation is the most essential and crucial calculations of the Software Development Life Cycle (SDLC) process. If done wisely, it may accumulate a large amount and time and if done badly, it may cost a lot of amounts. In this modern era of development, traditional methods of estimation do not stand a chance to provide output precisely. Constructive Cost Model (COCOMO)-2 is one of finest calculation methods for size and cost. This paper presents an automated early size estimation technique using Artificial Intelligence (AI). The categorization of the size estimation is into three parts, that is, UML diagrams/code folder mapping via COCOMO-2, COCOMO-2 metrics training, and the classification process to have an appropriate size. This paper focused on regression based training of AI which makes the estimation model more precise. For the evaluation, Mean Square Error (MSE) and Size estimation have been considered according to project samples. It has been seen that the proposed mechanism has attained a minimum MSE of 0.0115 and the difference in sizes is also not high. The comparison has also been done to depict the efficacy of the proposed work with A. B. Nassif et al. and S. Lohmor with B.B. Sagar for True positive rate and Mean square error.