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Software fault prediction using Mamdani type fuzzy inference system

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  • Ezgi Erturk
  • Ebru Akcapinar Sezer

Abstract

High quality software requires the occurrence of minimum number of failures while software runs. Software fault prediction is the determining whether software modules are prone to fault or not. Identification of the modules or code segments which need detailed testing, editing or, reorganising can be possible with the help of software fault prediction systems. In literature, many studies present models for software fault prediction using some soft computing methods which use training/testing phases. As a result, they require historical data to build models. In this study, to eliminate this drawback, Mamdani type fuzzy inference system (FIS) is applied for the software fault prediction problem. Several FIS models are produced and assessed with ROC-AUC as performance measure. The results achieved are ranging between 0.7138 and 0.7304; they are encouraging us to try FIS with the different software metrics and data to demonstrate general FIS performance on this problem.

Suggested Citation

  • Ezgi Erturk & Ebru Akcapinar Sezer, 2016. "Software fault prediction using Mamdani type fuzzy inference system," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 8(1), pages 14-28.
    • Handle: RePEc:ids:injdan:v:8:y:2016:i:1:p:14-28
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    References listed on IDEAS

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    1. Madjid Tavana & Farshad Azizi & Farzad Azizi & Majid Behzadian, 2013. "A fuzzy inference system with application to player selection and team formation in multi-player sports," Sport Management Review, Taylor & Francis Journals, vol. 16(1), pages 97-110, January.
    2. Hu, Q.P. & Xie, M. & Ng, S.H. & Levitin, G., 2007. "Robust recurrent neural network modeling for software fault detection and correction prediction," Reliability Engineering and System Safety, Elsevier, vol. 92(3), pages 332-340.
    3. Tavana, Madjid & Azizi, Farshad & Azizi, Farzad & Behzadian, Majid, 2013. "A fuzzy inference system with application to player selection and team formation in multi-player sports," Sport Management Review, Elsevier, vol. 16(1), pages 97-110.
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    Cited by:

    1. Vladyslav Sotnyk & Artem Kupchyn & Viktor Trynchuk & Vladimer Glonti & Larisa Belinskaja, 2022. "Fuzzy Logic Decision-Making Model for Technology Foresight," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 1, pages 139-159.
    2. Jasleen Kaur & Khushdeep Dharni, 2022. "Application and performance of data mining techniques in stock market: A review," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(4), pages 219-241, October.

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