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DOI: 10.14569/IJACSA.2017.080554
PDF

Predictive Approach towards Software Effort Estimation using Evolutionary Support Vector Machine

Author 1: Tahira Mahboob
Author 2: Sabheen Gull
Author 3: Sidrish Ehsan
Author 4: Bushra Sikandar

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 8 Issue 5, 2017.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: The project effort measurement is one of the most important estimates done in project management domain. This measure is done in advance using some traditional methods like Function Point analysis, Use case analysis, PERT analysis, Analogous, Poker, etc. Classical models have limitations that they are burdensome to implement, especially when there are LOC (lines of code) or objects’ count required in measurement. Sometimes historical information regarding a project is also considered to estimate the projects’ effort. But these estimates are then needed to be adjusted. The idea proposed in this research is to determine what factors regarding a project are directly related to the effort estimation. Other than that a model is proposed to predict the effort using minimum number of parameters in software project development.

Keywords: Correlation coefficient; Decision tree; Effort Estimation; Evolutionary Support Vector Machine; Software project management

Tahira Mahboob, Sabheen Gull, Sidrish Ehsan and Bushra Sikandar, “Predictive Approach towards Software Effort Estimation using Evolutionary Support Vector Machine” International Journal of Advanced Computer Science and Applications(IJACSA), 8(5), 2017. http://dx.doi.org/10.14569/IJACSA.2017.080554

@article{Mahboob2017,
title = {Predictive Approach towards Software Effort Estimation using Evolutionary Support Vector Machine},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.080554},
url = {http://dx.doi.org/10.14569/IJACSA.2017.080554},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {5},
author = {Tahira Mahboob and Sabheen Gull and Sidrish Ehsan and Bushra Sikandar}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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