An effective approach for software project effort and duration estimation with machine learning algorithms

During the last two decades, there has been substantial research performed in the field of software estimation using machine learning algorithms that aimed to tackle deficiencies of traditional and parametric estimation techniques, increase project success rates and align with modern development and project management approaches. Nevertheless, mostly due to inconclusive results and vague model building approaches, there are few or none deployments in practice. The purpose of this article is to narrow the gap between up-to-date research results and implementations within organisations by proposing effective and practical machine learning deployment and maintenance approaches by utilization of research findings and industry best practices. This was achieved by applying ISBSG dataset, smart data preparation, an ensemble averaging of three machine learning algorithms (Support Vector Machines, Neural Networks and Generalized Linear Models) and cross validation. The obtained models for effort and duration estimation are intended to provide a decision support tool for organisations that develop or implement software systems. (C) 2017 Elsevier Inc. All rights reserved.