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Using the Random Tree Classifier to Improve the Project’s Cost Predictability in the Earned Value Management: An Empirical Study

  • Ana C. da S. Fernandes
  • Adler Diniz de Souza
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 800)

Abstract

This paper proposes the selection of historical cost performance data of processes using the Random Tree classifier and the application of the calculation proposed by [20], to improve the predictability of projects cost. The proposed technique was evaluated through an empirical study, which evaluated the implementation of the proposed technique in 23 software development projects. The proposed technique has been applied in real projects with the aim of evaluating the accuracy and variation of the CPIAccum and consequently the EAC. Then it was compared to the EVM traditional technique. Hypotheses tests with 95% significance level were performed, and the proposed technique was more accurate and more stable (less variation) than the traditional technique for calculating the Cost Performance Index – CPI.

Keywords

Earned value management Cost performance index – CPI Random tree Measurement and analysis High maturity 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ana C. da S. Fernandes
    • 1
  • Adler Diniz de Souza
    • 1
  1. 1.Universidade Federal de ItajubaItajubaBrazil

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