Classification of Risk in Software Development Projects using Support Vector Machine

Authors

  • M. Zavvar Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran
  • A. Yavari Mazandaran University of Science and Technology
  • S.M Mirhassannia Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran
  • M.R. Nehi Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran
  • A. Yanpi Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran
  • M.H. Zavvar Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran

Keywords:

Classification, Risk, Software, Support Vector Machine, Area under Curve,

Abstract

Traditionally, the lack of confidence in the system life cycle is expressed using the concept of risk. Nowadays, software development projects face various risks. However, the estimation and classification of risk, increased estimation of accuracy and reduced of uncertainty ultimately improve project outcomes. Therefore, in this paper, a Support Vector Machine (SVM) is used to model risk classification in software development projects. The proposed algorithm is compared with other methods in the literature such as Self Organizing Map (SOM) and K-Means based on measures of Classification Accuracy Rate (CAR) and Area Under Curve (AUC). According to the results, the proposed method exhibits superior CAR and AUC.

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Author Biography

M. Zavvar, Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran

Mohammad Zavvar has got M.S.c in Software Engineering from the Sari Azad University. And now, to research and teaching in the field of programming, image processing, fuzzy systems, neural networks, data mining, software engineering and algorithm optimization.  He also has published numerous articles on various topics in the field of software engineering and information technology in conferences and international journals.

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Published

2017-01-01

How to Cite

Zavvar, M., Yavari, A., Mirhassannia, S., Nehi, M., Yanpi, A., & Zavvar, M. (2017). Classification of Risk in Software Development Projects using Support Vector Machine. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(1), 1–5. Retrieved from https://jtec.utem.edu.my/jtec/article/view/857

Issue

Vol. 9 No. 1: January - March 2017

Section

Articles

License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)