IDEAS home Printed from https://ideas.repec.org/a/wsi/ijitdm/v08y2009i04ns0219622009003715.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S0219622009003715
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1142/S0219622009003715?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. David H. Wolpert & William G. Macready, 1995. "No Free Lunch Theorems for Search," Working Papers 95-02-010, Santa Fe Institute.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jui-Sheng Chou & Dinh-Nhat Truong & Chih-Fong Tsai, 2021. "Solving Regression Problems with Intelligent Machine Learner for Engineering Informatics," Mathematics, MDPI, vol. 9(6), pages 1-25, March.
    2. Sevvandi Kandanaarachchi & Mario A Munoz & Rob J Hyndman & Kate Smith-Miles, 2018. "On normalization and algorithm selection for unsupervised outlier detection," Monash Econometrics and Business Statistics Working Papers 16/18, Monash University, Department of Econometrics and Business Statistics.
    3. Kamran Zolfi, 2023. "Gold rush optimizer: A new population-based metaheuristic algorithm," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 33(1), pages 113-150.
    4. Y.C. Ho & D.L. Pepyne, 2002. "Simple Explanation of the No-Free-Lunch Theorem and Its Implications," Journal of Optimization Theory and Applications, Springer, vol. 115(3), pages 549-570, December.
    5. Murtadha Al-Kaabi & Virgil Dumbrava & Mircea Eremia, 2022. "A Slime Mould Algorithm Programming for Solving Single and Multi-Objective Optimal Power Flow Problems with Pareto Front Approach: A Case Study of the Iraqi Super Grid High Voltage," Energies, MDPI, vol. 15(20), pages 1-33, October.
    6. Abdel-Rahman Hedar & Emad Mabrouk & Masao Fukushima, 2011. "Tabu Programming: A New Problem Solver Through Adaptive Memory Programming Over Tree Data Structures," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 10(02), pages 373-406.
    7. Murtadha Al-Kaabi & Virgil Dumbrava & Mircea Eremia, 2024. "Multi Criteria Frameworks Using New Meta-Heuristic Optimization Techniques for Solving Multi-Objective Optimal Power Flow Problems," Energies, MDPI, vol. 17(9), pages 1-39, May.
    8. Muangkote, Nipotepat & Sunat, Khamron & Chiewchanwattana, Sirapat & Kaiwinit, Sirilak, 2019. "An advanced onlooker-ranking-based adaptive differential evolution to extract the parameters of solar cell models," Renewable Energy, Elsevier, vol. 134(C), pages 1129-1147.
    9. Sharifian, Yeganeh & Abdi, Hamdi, 2023. "Solving multi-area economic dispatch problem using hybrid exchange market algorithm with grasshopper optimization algorithm," Energy, Elsevier, vol. 267(C).
    10. Díaz–Pachón, Daniel Andrés & Sáenz, Juan Pablo & Rao, J. Sunil, 2020. "Hypothesis testing with active information," Statistics & Probability Letters, Elsevier, vol. 161(C).
    11. Zhang, Xueying & Li, Ruixian & Zhang, Bo & Yang, Yunxiang & Guo, Jing & Ji, Xiang, 2019. "An instance-based learning recommendation algorithm of imbalance handling methods," Applied Mathematics and Computation, Elsevier, vol. 351(C), pages 204-218.
    12. Peter F. Stadler & Gunjter P. Wagner, 1996. "The Algebraic Theory of Recombination Spaces," Working Papers 96-07-046, Santa Fe Institute.
    13. Chen, Xu & Lu, Qi & Yuan, Ye & He, Kaixun, 2024. "A novel derivative search political optimization algorithm for multi-area economic dispatch incorporating renewable energy," Energy, Elsevier, vol. 300(C).
    14. L. Ingber, 1996. "Adaptive simulated annealing (ASA): Lessons learned," Lester Ingber Papers 96as, Lester Ingber.
    15. Christopher Ifeanyi Eke & Azah Anir Norman & Liyana Shuib, 2021. "Multi-feature fusion framework for sarcasm identification on twitter data: A machine learning based approach," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-32, June.
    16. Aktaş, Dilay & Lokman, Banu & İnkaya, Tülin & Dejaegere, Gilles, 2024. "Cluster ensemble selection and consensus clustering: A multi-objective optimization approach," European Journal of Operational Research, Elsevier, vol. 314(3), pages 1065-1077.
    17. William G. Macready & David H. Wolpert, 1995. "What Makes an Optimization Problem Hard?," Working Papers 95-05-046, Santa Fe Institute.
    18. Galioto, Francesco & Battilani, Adriano, 2021. "Agro-economic simulation for day by day irrigation scheduling optimisation," Agricultural Water Management, Elsevier, vol. 248(C).
    19. Agarwal, Anurag & Colak, Selcuk & Eryarsoy, Enes, 2006. "Improvement heuristic for the flow-shop scheduling problem: An adaptive-learning approach," European Journal of Operational Research, Elsevier, vol. 169(3), pages 801-815, March.
    20. Murtadha Al-Kaabi & Virgil Dumbrava & Mircea Eremia, 2022. "Single and Multi-Objective Optimal Power Flow Based on Hunger Games Search with Pareto Concept Optimization," Energies, MDPI, vol. 15(22), pages 1-31, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wsi:ijitdm:v:08:y:2009:i:04:n:s0219622009003715. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscinet.com/ijitdm/ijitdm.shtml .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.