Journal of Software Engineering and Applications, 2020, 13, 143-160 https://www.scirp.org/journal/jsea ISSN Online: 1945-3124 ISSN Print: 1945-3116 DOI: 10.4236/jsea.2020.137010 Jul. 17, 2020 143 Journal of Software Engineering and Applications Software Effort Prediction Using Ensemble Learning Methods Omar H. Alhazmi, Mohammed Zubair Khan Department of Computer Science, College of Computer Science and Engineering Taibah University, Madinah, KSA Abstract Software Cost Estimation (SCE) is an essential requirement in producing software these days. Genuine accurate estimation requires cost-and-efforts factors in delivering software by utilizing algorithmic or Ensemble Learning Methods (ELMs). Effort is estimated in terms of individual months and length. Overestimation as well as underestimation of efforts can adversely af- fect software development. Hence, it is the responsibility of software devel- opment managers to estimate the cost using the best possible techniques. The predominant cost for any product is the expense of figuring effort. Subse- quently, effort estimation is exceptionally pivotal and there is a constant need to improve its accuracy. Fortunately, several efforts estimation models are available; however, it is difficult to determine which model is more accurate on what dataset. Hence, we use ensemble learning bagging with base learner Linear regression, SMOReg, MLP, random forest, REPTree, and M5Rule. We also implemented the feature selection algorithm to examine the effect of feature selection algorithm BestFit and Genetic Algorithm. The dataset is based on 499 projects known as China. The results show that the Mean Mag- nitude Relative error of Bagging M5 rule with Genetic Algorithm as Feature Selection is 10%, which makes it better than other algorithms. Keywords Software Cost Estimation (SCE), Ensemble Learning, Bagging, Linear Regression, SMOReg, REPTree, M5 Rule 1. Introduction For software developers the quality of a software product is vital, and software cost estimation efforts help developers to maintain good quality. Software cost estimation in terms of the persons-months and time to complete the project is How to cite this paper: Alhazmi, O.H. and Khan, M.Z. (2020) Software Effort Predic- tion Using Ensemble Learning Methods. Journal of Software Engineering and Ap- plications, 13, 143-160. https://doi.org/10.4236/jsea.2020.137010 Received: June 4, 2020 Accepted: July 14, 2020 Published: July 17, 2020 Copyright © 2020 by author(s) and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY 4.0). http://creativecommons.org/licenses/by/4.0/ Open Access