Anzeige
Erschienen in:
2018 | OriginalPaper | Buchkapitel
Software Project Management: Resources Prediction and Estimation Utilizing Unsupervised Machine Learning Algorithm
verfasst von : Mohammad Masoud, Wejdan Abu-Elhaija, Yousef Jaradat, Ismael Jannoud, Loai Dabbour
Erschienen in: 8th International Conference on Engineering, Project, and Product Management (EPPM 2017)
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Abstract
Software project effort estimation is a major process in software development cycle. This process helps in decision making in resource allocation and distribution. In this work, a new effort estimation clustering method based on estimation maximization soft-clustering unsupervised machine learning algorithm is proposed. This model classifies any software project into one of four categories. An enterprise will accept to develop a software project if this project is clustered into a class that requires resources equal or less than the enterprises resources. The new model helps in decision making process in one hand and helps consumers in assigning projects to a developing enterprise in the other hand. COCOMO dataset has been used to implement, deploy and test the model. The propose model has been compared with K-means algorithm to show the differences between soft and hard clustering. The paper results show that soft-clustering has the ability to estimate efforts like any supervised machine learning algorithms.