Extreme Learning Machine Applied to Software Development Effort Estimation (original) (raw)

The project management process has been used in the area of Software Engineering to support project managers to keep projects under control. One of the essential processes in Software Engineering is to conduct an accurate and reliable estimation of the required effort to complete the project. This article's objectives are: i) to identify the variables that influence the estimation based on the correlation, and ii) to apply the Extreme Learning Machine-ELM model for effort estimation and compare it with the literature models. Thus, it was investigated which technique has better effort prediction accuracy. The models were compared with each other based on predictive precision in the criterion of absolute mean residue (MAR) and statistical tests. The main findings in this study were: i) important variables for effort estimation and; ii) the results indicated that the ELM model presents the best results compared to the models in the literature for estimating software design effort. In this way, the use of Machine Learning techniques in the effort estimation process can increase the chances of success in the accuracy of the time estimates and the project's costs. INDEX TERMS Extreme learning machine, machine learning, effort estimation, software development, project management. WYLLIAMS SANTOS received the M.Sc. and Ph.D. degrees in computer science from the Informatics Center (CIn), Federal University of Pernambuco (UFPE), Brazil, in 2011 and 2018, respectively. From 2015 to 2016, he undertook his joint Ph.D. research with the Department of Computer Science and Information Systems (CSIS), University of Limerick, Ireland, and in collaboration with Lero-the Irish Software Research Centre. He is currently an Adjunct Professor with the University of Pernambuco (UPE), Brazil, where he leads the REACT Research Laboratories. His research interests include the management of software projects, agile software development, technical debt, and industry-academia collaboration.