Software Fault Prediction Based on Interval Type-2 Intuitionistic Fuzzy Logic System


  • Imo J. Eyoh University of Uyo, Uyo, Akwa Ibom State, Nigeria.
  • Edward N. Udo University of Uyo, Uyo, Akwa Ibom State, Nigeria.
  • Ini J. Umoeka University of Uyo, Uyo, Akwa Ibom State, Nigeria.



Software Fault Prediction, Fuzzy Logic System, Interval Type-2 Intuitionistic Fuzzy Logic System


With the continuous expansion and innovations in modern software development, the rate at which defects are present in software is directly proportional to how sophisticated and complex the software tends to be. Software fault prediction, therefore, continues to remain an important area of research in software engineering especially as new modeling algorithms are still emerging. In spite of the fact that the potential implementations of fuzzy set theory in software fault prediction have been explored in the past, to the best knowledge of the authors, it has not yet examined how interval type-2 intuitionistic fuzzy sets with membership and non-membership degrees could be used with parameter optimization in this domain. Therefore, this work aims to adopt an interval type-2 intuitionistic fuzzy logic system to predict fault in the requirement phase of Software Development Life Cycle. Intuitionistic fuzzy logic system deals with uncertainty using separate degrees of membership and non-membership of an element to a set as well as hesitation index, therefore becoming more appropriate and flexible tool to deal with imprecision and vagueness in data. Experimental analyses show that the obtained prediction outputs are very close to the actual outputs which confirm that the proposed approach is a more realistic alternative for software fault prediction.


Ali, A., Jawawi, D., Isa M., Babar, M., “Technique for Early Reliability Prediction of Software Components Using Behaviour Models”. PLoS ONE 11(9): e0163346, 2016, doi:10.1371/journal.pone.0163346.

Kaur, R. and Sharma, E., “Various Techniques to Detect and Predict Faults in Software System: Survey”. International Journal on Future Revolution in Computer Science and Communication Engineering, 2018, 4(2), 330 – 336.

Singh, P., Pal, N., Verma, S. and Vyas, O., “Fuzzy Rule-Based Approach for Software Fault Prediction”. In proceedings of IEEE Transactions on Systems, MAN and Cybernetics: Systems, 2016, 47(5), 826 – 837.

Ranjan, P., Kumar, S. and Kumar, U., “Software Fault Prediction Using Computational Intelligence Techniques: A Survey”. Indian Journal of Science and Technology, 2017, 10(8), 1 – 9.

Auju, A. J. and Judith, J. E., “Software Defect Prediction using Efficient Classification Algorithm”. International Journal of Recent Technology and Engineering, 2019, 8(3), 301 – 304.

Fan, G., Diao, X., Yu, H., Yang, K. and Chen, L., “Software Defect Prediction via Attention-Based Recurrent Neural Network”. Hindawi Scientific Programming, 2019.

Adak, M., Software Defect Prediction using Data Mining Fuzzy Logic. In Proceeding of 6th International Conference on Digital Information, Networking and Wireless Communication, Beirut, Lebanon, 2018, 65 – 69.

Jayanthi, R., Lilly, F. and Arya, Arti. “A review of Software Defect Prediction Techniques using Product Metrics”. International Journal of Database Theory and Applications, 2017, 10(1), 163 – 174.

Malhotra, R and Jain, A., “Fault Prediction Using Statistical and Machine Learning Methods for Improving Software Quality”. Journal of Information Processing Systems, 2012, 8(2), 241-262.

Nair, A., Arya, A., Shrivastava, A. and Shrivastava, V., “Software Fault Prediction using Intelligence Techniques”. International Journal of Advanced Research in Computer Science, 2013, 4(11), 166 – 169.

Moeyersoms, J., deFortumy, E., Dejaeger, K. and Beasens, B., “Comprehensible Software Fault and Effort Prediction: A Data Mining Approach”. Journal of Systems and Software, 2015, 100, 80 – 90.

Arasteh, B., “Software Fault Prediction using Combination of Neural Network and naïve Bayes Algorithm”. Journal of Networking Technology, 2018, 9(3), 94 -101.

Kakkar, M., Jain, S., Bansal, A. and Grover, P., “Fuzzy Logic-Based Model to Predict Per phase Software Defect”. International Journal of Innovative Technology and Exploring Engineering, 2019, 8(9), 36 – 41.

Rathore. S. and Kumar, S., “A Study on Software Fault Prediction Techniques”. Artificial Intelligence Review, 2017, 51, 255 – 327.

Catal, C. and Diri, B., “Investigating the Effect of Dataset Size, Metrics Sets and Feature Selection Techniques on Software Fault Prediction Problem”. Elsevier, 2009, pp. 1040-1058.

Basili, V., Briand, L and Melo, W., “A validation of Object-Oriented Design Metrics as Quality Indicators”. IEEE Transactions on Software Engineering, 22(10), 1996, pp. 751–761.

Khoshgoftaar, T., Allen, E., Hudepohl, J. and Aud, S., “Application of Neural Networks to Software Quality Modeling of a very large Telecommunications System", IEEE Transactions on Neural Networks, 1997, 8(4), 902–909.

Khoshgoftaar, T., Allen, E., and Deng, J., “Using Regression Trees to Classify Fault-Prone Software Modules”. IEEE Transactions on Reliability 51 (4), 2002, 455–462.

Parvinder, S., Sunil, K., Satpreet, S., Simranjit, K., Manpreet, K. and S. Gurvinder, S., “A Study on Early Prediction of Fault Proneness in Software Modules using Genetic Algorithm”. World Academy of Science, Engineering and Technology, International Journal of Computer and Information Engineering, 2010, 4(12), 1891 – 1896.

Kanmani, S., Uthariaraj, V., Sankaranarayanan, V. and Thambidurai, P., “Object-Oriented Software Fault Prediction using Neural Networks”. Information and Software Technology, 2007, 49(5), 483-492.

Reshi, J. and Singh, S., “Predicting Software Defects through SVM: An Empirical Approach”. International Journal of Scientific Research and Development, 2017, 5(5), 1835 – 1838.

Jin, C., Jin, S. and Ye, J., “Artificial Neural Network-Based Metric Selection for Software Fault-Prone Prediction Model”. IET Software, 2012, 6(6), 479–487.

Kumar, L. and Rath S., “Neuro-Genetic Approach for Predicting Maintainability Using Chidamber and Kemerer Software Metrics Suite”. In: Unger H., Meesad P., Boonkrong S. (eds) Recent Advances in Information and Communication Technology. Advances in Intelligent Systems and Computing, vol 361. Springer, Cham, 2015, 31 – 40.

Erturk, E. and Sezer, E., “Iterative Software Fault Prediction with a Hybrid Approach”, Applied Soft Computing, 2016, vol. 49, 1020-1033.

Shepperd, M., Kadoda, G., “Comparing Software Prediction Techniques using Simulation”. IEEE Transaction in. Software. Engineering, 2001, 27 (11), 1014–1022.

Pandey, A., and Goyal, N., “Fault Prediction Model by Fuzzy Profile Development of Reliability Relevant Software Metrics”. International Journal of Computer Applications, 2010, 11, 34–41.

Erturk, E. and Sezer, E., “Software Fault Prediction using Fuzzy Inference System and Object-Oriented Metrics”. In proceedings of the IASTED International Conference on Software Engineering, Innsbruck, Austra, 2014, 101 – 108.

Chatterjee, S., and Maji, B., “A New Fuzzy Rule-Based Algorithm for Estimating Software Faults in Early Phase of Development”. Soft Computing, 2016, 20, 4023–4035.

Jaikumar, M. and Ramani, A., “Software Defect Prediction using Fuzzy Logic System”. International Journal of Innovations and Advancement in Computer Science, 2017, 6(3), 118 – 124.

Chatterjee, S., Maji, B. and Pham, H., “A Fuzzy Rule-Based Generation Algorithm in Interval Type-2 Fuzzy Logic System for Fault Prediction in the Early Phase of Software Development”. Journal of Experimental and Theoretical Artificial Intelligence, 2018, 31(3), 369 – 391.

Eyoh, I., John, R. and De Macre, G., “Interval Type-2 Intuitionistic Fuzzy Logic System for Non-linear System Prediction”. In Proceedings of IEEE International Conference on Systems, Man and Cybernetics, Budapest, Hungary, 2016, 1063 – 1068.

Son, L., Pritam, N., Khari, M., Kumar, R., Phuong, P. and Thong, P., “Empirical Study of Software Defect Prediction: A Systematic Mapping”. Symmetry, 2019, 11(2), 212,

Atanassov, K. T., “Intuitionistic fuzzy sets,” Fuzzy sets and Systems, vol. 20, no. 1, pp. 87–96, 1986.

Eyoh, I. J., Umoh, U. A., Inyang, U. G., and Eyoh, J. E., “Derivative-Based Learning of Interval Type-2 Intuitionistic Fuzzy Logic Systems for Noisy Regression Problems”. International Journal of Fuzzy Systems, 2020, 1-13.

Imo Eyoh, Jeremiah Eyoh, and Ini Umoeka., “Interval Type-2 Intuitionistic Fuzzy Logic System for Forecasting the Electricity Load”. International Journal of Advances in Scientific Research and Engineering, IJASRE (ISSN: 2454 - 8006), 6(10), (2020). 38-51. 2020, 33903

Eyoh, I., Eyoh, J. and kalawsky, R., “Interval Type-2 Intuitionistic Fuzzy Logic for Time Series and Identification Problems: A Comprehensive Study”. International Journal of Fuzzy Logic Systems, 2020, 10(1), 1 – 17.

Luo, C., Tan, C., Wang, X., and Zheng, Y., “An evolving recurrent interval type-2 intuitionistic fuzzy neural network for online learning and time series prediction”. Applied Soft Computing, 2019, 78, 150-163.

Eyoh, I., John, R., De Maere, G., “Time series forecasting with interval type-2 intuitionistic fuzzy logic systems,” In: 2017 IEEE international conference on fuzzy systems (FUZZ-IEEE). IEEE. 2017a.

Imo Eyoh, Jeremiah Eyoh and Roy Kalawsky, “Interval Type-2 Intuitionistic Fuzzy Logic System for Time Series and Identification Problems - A Comparative Study”, International Journal of Fuzzy Logic Systems (IJFLS), 2020, Vol.10, No.1, DOI: 10.5121/ijfls.2020.10101.

Eyoh, I., John, R., De Maere, G., “Extended Kalman filter-based learning of interval type-2 intuitionistic fuzzy logic system. In: 2017 IEEE international conference on systems, man, and cybernetics (SMC). IEEE, 2017b.

Eyoh, I., John, R., De Maere, G., “Interval type-2 intuitionistic fuzzy logic systems—a comparative evaluation”. In: International conference on information processing and management of uncertainty in knowledge-based systems. Springer, Cham (2018).

Eyoh, I., Eyoh, J., Umoh, U., and Kalawsky, R., “A Sliding Mode Control Learning of Interval Type-2 Intuitionistic Fuzzy Logic for Non-Linear System Prediction”. Solid State Technology, 2020, 63(6), 7793-7811.

Eyoh, I., John, R., Maere, G.D., Kayacan, E., “Hybrid learning for interval type-2 intuitionistic fuzzy logic systems as applied to identification and prediction problems”. IEEE Trans Fuzzy Syst, 26(5), 2672–2685, 2018.

Yuan, W., and Chao, L., “Online evolving interval type-2 intuitionistic fuzzy LSTM-neural networks for regression problems”. IEEE Access, 2019, 7, 35544-35555.

Eyoh, I., John, R., Maere, G.D., “Interval type-2 A-intuitionistic fuzzy logic for regression problems”. IEEE Trans Fuzzy Syst 2018, 26(4), 2396–2408.

Ebrahimnejad, A., and Verdegay, J. L., “An efficient computational approach for solving type-2 intuitionistic fuzzy numbers-based transportation problems”. International Journal of Computational Intelligence Systems, 2016, 9(6), 1154-1173.

Nguyen, D. D., Ngo, L. T. and Pham, L. T., “Interval type-2 fuzzy c- means clustering using intuitionistic fuzzy sets”, in IEEE Third World Congress on Information and Communication Technologies (WICT), 2013, pp. 299–304.

Ha´jek, P., Olej, V., “Intuitionistic fuzzy neural network: the case of credit scoring using text information”, pp. 337–346. Cham, Springer, 2015.

Tai, K., El-Sayed, A.-R., Biglarbegian, M., Gonzalez, C. I., Castillo, O. and Mahmud, S., “Review of recent type-2 fuzzy controller applications," Algorithms, vol. 9, no. 2, p. 39, 2016.

Begian, M. B., Melek, W. W. and Mendel, J. M., “Parametric design of stable type-2 tsk fuzzy systems," in IEEE Annual Meeting of the North American Fuzzy Information Processing Society, (NAFIPS), pp. 1-6, 2008.

Nie M. and Tan, W. W., “Towards an efficient type-reduction method for interval type-2 fuzzy logic Systems”, in Fuzzy Systems, 2008. FUZZ-IEEE 2008. (IEEE World Congress on Computational Intelligence). IEEE International Conference on, pp. 1425-1432, IEEE, 2008.

Wu D. and Tan, W. W., “Computationally efficient type-reduction strategies for a type-2 fuzzy logic Controller”, in

Fuzzy Systems, 2005. FUZZ’05. The 14th IEEE International Conference on, pp. 353-358, IEEE, 2005.

Fenton, N., Neil, M., Marsh, W., Hearty, P., Radlinski, L. and Krause, P., “On the Effectiveness of Early Life Cycle Defect Prediction with Bayesian Nets”. Empirical Software Engineering, 2008. 13(5), 499 – 537.

Pandey, A. and Goyal, N., “Multistage Model for Residual Fault Prediction”, Studies in Fuzziness and Soft Computing, Springer, 2013, 59–80.

Ini Umoeka, Imo Eyoh, Edward Udo and Veronica Akwukwuma, “Optimization of Interval Type-2 Fuzzy Logic System for Software Reliability Prediction”, International Journal of Engineering Research and Advanced Technology, 2020, Vol. 6, No. 11, 1-12. DOI: 10.31695/IJERAT, 2020, 3665.



How to Cite

Imo J. Eyoh, Edward N. Udo, & Ini J. Umoeka. (2021). Software Fault Prediction Based on Interval Type-2 Intuitionistic Fuzzy Logic System. International Journal of Advances in Scientific Research and Engineering (IJASRE), 7(5), 10-24.