Research Article Open Access

Optimizing Software Effort Estimation Models Using Back-Propagation Versus Radial Base Function Networks

Abdel Karim Baareh1
  • 1 Ajloun University College, Jordan
Journal of Computer Science
Volume 15 No. 3, 2019, 321-331

DOI: https://doi.org/10.3844/jcssp.2019.321.331

Submitted On: 21 November 2018 Published On: 11 March 2019

How to Cite: Baareh, A. K. (2019). Optimizing Software Effort Estimation Models Using Back-Propagation Versus Radial Base Function Networks. Journal of Computer Science, 15(3), 321-331. https://doi.org/10.3844/jcssp.2019.321.331

Abstract

Software development effort estimation becomes a very important and vital tool for many researchers in different fields. Software estimation used in controlling, organizing and achieving projects in the required time and cost to avoid the financial punishments due to the time delay and other different circumstances that may happen. Good project cost estimation will lead to project success and reduce the risk of project failure. In this paper, two neural network models are used, the Back-propagation algorithm versus the redial base algorithm. A comparison is done between the suggested models to find the best model that can reduce the project risks related to time and increase the profit by achieving the demands of the required project in time. The two models are implemented on a 60 of NASA public dataset, divided into 45 data samples for training and 15 data samples for testing. From the result obtained we can clearly say that the performance of the back-propagation neural network in training and testing cases is actually better than the radial base function, so the back-propagation algorithm can be recommended as a useful tool in the software effort and cost estimation.

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Keywords

  • Effort Estimation
  • NASA Software
  • Artificial Neural Network
  • Back-Propagation
  • Radial Base Function