Software Project Management: Resources Prediction and Estimation Utilizing Unsupervised Machine Learning Algorithm

  • Mohammad Masoud
  • Wejdan Abu-Elhaija
  • Yousef Jaradat
  • Ismael Jannoud
  • Loai Dabbour
Conference paper
Part of the Lecture Notes in Mechanical Engineering book series (LNME)


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.


Effort estimation Estimation maximization Soft-clustering Maximum likelihood K-means clustering 


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Mohammad Masoud
    • 1
  • Wejdan Abu-Elhaija
    • 1
  • Yousef Jaradat
    • 1
  • Ismael Jannoud
    • 1
  • Loai Dabbour
    • 2
  1. 1.Electrical Engineering DepartmentAl-Zaytoonah University of JordanAmmanJordan
  2. 2.Architecture Engineering DepartmentAl-Zaytoonah University of JordanAmmanJordan

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