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Empirical Evaluation Of Classifiers For Software Risk Management

Author

Listed:
  • YI PENG

    (School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, 610054, P. R. China)

  • GANG KOU

    (School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, 610054, P. R. China)

  • GUOXUN WANG

    (School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, 610054, P. R. China)

  • HONGGANG WANG

    (Department of Electrical and Computer Engineering, University of Massachusetts, Dartmouth, USA)

  • FRANZ I. S. KO

    (Department of Computer and Multimedia, Dongguk University, Korea)

Abstract

Software development involves plenty of risks, and errors exist in software modules represent a major kind of risk. Software defect prediction techniques and tools that identify software errors play a crucial role in software risk management. Among software defect prediction techniques, classification is a commonly used approach. Various types of classifiers have been applied to software defect prediction in recent years. How to select an adequate classifier (or set of classifiers) to identify error prone software modules is an important task for software development organizations. There are many different measures for classifiers and each measure is intended for assessing different aspect of a classifier. This paper developed a performance metric that combines various measures to evaluate the quality of classifiers for software defect prediction. The performance metric is analyzed experimentally using 13 classifiers on 11 public domain software defect datasets. The results of the experiment indicate that support vector machines (SVM), C4.5 algorithm, andK-nearest-neighbor algorithm ranked the top three classifiers.

Suggested Citation

  • Yi Peng & Gang Kou & Guoxun Wang & Honggang Wang & Franz I. S. Ko, 2009. "Empirical Evaluation Of Classifiers For Software Risk Management," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 8(04), pages 749-767.
    • Handle: RePEc:wsi:ijitdm:v:08:y:2009:i:04:n:s0219622009003715
    DOI: 10.1142/S0219622009003715
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    References listed on IDEAS

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    1. David H. Wolpert & William G. Macready, 1995. "No Free Lunch Theorems for Search," Working Papers 95-02-010, Santa Fe Institute.
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    Cited by:

    1. Mario Silic & Andrea Back, 2016. "The Influence of Risk Factors in Decision-Making Process for Open Source Software Adoption," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(01), pages 151-185, January.
    2. Stan Lipovetsky & Igor Mandel, 2017. "Coefficients of Structural Association," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(02), pages 285-313, March.
    3. Jianping Li & Minglu Li & Dengsheng Wu & Qianzhi Dai & Hao Song, 2016. "A Bayesian Networks-Based Risk Identification Approach for Software Process Risk: The Context of Chinese Trustworthy Software," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(06), pages 1391-1412, November.
    4. Maysam Eftekhary & Peyman Gholami & Saeed Safari & Mohammad Shojaee, 2012. "Ranking Normalization Methods for Improving the Accuracy of SVM Algorithm by DEA Method," Modern Applied Science, Canadian Center of Science and Education, vol. 6(10), pages 1-26, October.

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