Optimizing Software Effort Estimation Models Based On Metaheuristic Methods: A Proposed Framework

Abstract

Software effort estimation are part of the field of project management in software that is very important for development efforts. Software development planning is something very complex and serious, which determines the success of a software project. Because of the lack of good requirements and information, it causes software project failures. Although there are many studies that aim to solve the problem of noisy, irrelevant and excessive data to achieve accuracy. The purpose of this study is to combine metaheuristic optimization techniques as a framework for using Machine Learning models. By proposing a hybrid estimation model based on a combination of the Satin Bowerbird Optimizer (SBO) algorithm and Support Vector Regression (SVR) to improve the accuracy of software estimation efforts. This study is to determine the effort estimation and duration estimation. The proposed framework is based on theoretical concepts. the proposed model will be tested using a heterogeneous dataset, namely the ISBSG dataset. the results of the study are expected to be used as decision making as the initial planning of software project development.