Software Fault Prediction using Fuzzy C-Means Clustering Algorithm
DOI:
https://doi.org/10.15662/IJEETR.2026.0802302Keywords:
Fuzzy C-Means clustering, software fault prediction, cluster centerAbstract
Clustering is a Unsupervised technique, its used for fault prediction in software modules. In predicting fault in program modules using a hierarchical agglomerative algorithm and construct the dissimilarity matrix between each two objects. We propose a Fuzzy C-Means Clustering based software fault prediction approach. The initial cluster center are applied to the input of Fuzzy C-Means Algorithm. The concept of clustering gain has been used to determine the quality of cluster for the evaluation of Fuzzy C-Means Clustering Algorithm. Software metrics and fault data belonging to a previous software version are used to build the software fault prediction model for the next release of the software. However there are certain cases when previous fault data are not present. In other words predicting the fault-proneness of program modules when the fault labels for modules are unavailable is a challenging task frequently arised in the software industry There is need to develop some methods to build the software fault prediction model based on unsupervised learning which can help to predict the fault–proneness of a program modules when fault labels for modules are not present. One of the such method is use of clustering techniques. Unsupervised techniques like clustering may be used for fault prediction in software modules, more so in those cases where fault labels are not available. In this study, we propose a Fuzzy c-means clustering based software fault prediction approach for this challenging problem.
References
1. C. Catal, U. Sevim, and B. Diri, “Clustering and Metrics Threshold Based Software Fault Prediction of Unlabeled Program Modules,” Proc. Sixth Int’l Conf. Information Technology: New Generations, pp. 199-204, 2009.
2. S. Zhong, T.M. Khoshgoftaar, and N. Seliya, “Unsupervised Learning for Expert-Based Software Quality Estimation,” Proc. IEEE Eighth Int’l Symp. High Assurance Systems Eng., pp. 149-155, 2004.
3. S. Zhong, T.M. Khoshgoftaar, and N. Seliya, “Analyzing Software Measurement Data with Clustering Techniques,” IEEE Intelligent Systems, vol. 19, no. 2, pp. 20-27, Mar./Apr. 2004.
4. N. Seliya and T.M. Khoshgoftaar, “Software Quality Classification Modeling Using the PRINT Decision Algorithm,” Proc. IEEE 14th Int’l Conf. Tools with Artificial Intelligence, pp. 365-374, 2002.
5. J. Han and M. Kamber, Data Mining Concepts and Techniques, second ed, pp. 401-404. Morgan Kaufmann Publishers, 2007.
6. M. Ester, H.P. Kriegel, J. Sander, and X.Xu., “A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise,” Proc. Second Int’l Conf. Knowledge Discovery and Data Mining (KDD ’96), pp. 226- 231, 1996. http://archive.ics.uci.edu/ml/datasets/Iris, 2012.
7. P.S. Bishnu and V. Bhattacharjee, “A New Initialization Method for KMeans Algorithm Using Quad Tree,” Proc. Nat’l Conf. Methods and Models in Computing (NCM2C), pp. 73-81, 2008.
8. M. Laszlo and S. Mukherjee, “A Genetic Algorithm Using Hyper-Quadtrees for Low-Dimensional K-Means Clustering,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 4, pp. 533-543, Apr. 2006.
9. D. Pelleg and A. Moore, “X-Means: Extending K-Means with Efficient Estimation of the Number of Cluster,” Proc. 17th Int’l Conf. Machine Learning, pp. 727-734, 2000.
10. C.Nagarajan and M.Madheswaran - ‘Stability Analysis of Series Parallel Resonant Converter with Fuzzy Logic Controller Using State Space Techniques’- Taylor &Francis, Electric Power Components and Systems, Vol.39 (8), pp.780-793, May 2011. DOI: 10.1080/15325008.2010.541746
11. C.Nagarajan and M.Madheswaran - ‘Experimental verification and stability state space analysis of CLL-T Series Parallel Resonant Converter’ - Journal of Electrical Engineering, Vol.63 (6), pp.365-372, Dec.2012. DOI: 10.2478/v10187-012-0054-2
12. C.Nagarajan and M.Madheswaran - ‘Performance Analysis of LCL-T Resonant Converter with Fuzzy/PID Using State Space Analysis’- Springer, Electrical Engineering, Vol.93 (3), pp.167-178, September 2011. DOI 10.1007/s00202-011-0203-9
13. S.Tamilselvi, R.Prakash, C.Nagarajan,“Solar System Integrated Smart Grid Utilizing Hybrid Coot-Genetic Algorithm Optimized ANN Controller” Iranian Journal Of Science And Technology-Transactions Of Electrical Engineering, DOI10.1007/s40998-025-00917-z,2025
14. S.Tamilselvi, R.Prakash, C.Nagarajan,“ Adaptive sliding mode control of multilevel grid-connected inverters using reinforcement learning for enhanced LVRT performance” Electric Power Systems Research 253 (2026) 112428, doi.org/10.1016/j.epsr.2025.112428
15. S.Thirunavukkarasu, C. Nagarajan, 2024, “Performance Investigation on OCF and SCF study in BLDC machine using FTANN Controller," Journal of Electrical Engineering And Technology, Volume 20, pages 2675–2688, (2025), doi.org/10.1007/s42835-024-02126-w
16. C. Nagarajan, M.Madheswaran and D.Ramasubramanian- ‘Development of DSP based Robust Control Method for General Resonant Converter Topologies using Transfer Function Model’- Acta Electrotechnica et Informatica Journal , Vol.13 (2), pp.18-31,April-June.2013, DOI: 10.2478/aeei-2013-0025.
17. C.Nagarajan and M.Madheswaran - ‘DSP Based Fuzzy Controller for Series Parallel Resonant converter’- Springer, Frontiers of Electrical and Electronic Engineering, Vol. 7(4), pp. 438-446, Dec.12. DOI 10.1007/s11460-012-0212-0.
18. C.Nagarajan and M.Madheswaran - ‘Experimental Study and steady state stability analysis of CLL-T Series Parallel Resonant Converter with Fuzzy controller using State Space Analysis’- Iranian Journal of Electrical & Electronic Engineering, Vol.8 (3), pp.259-267, September 2012.
19. C.Nagarajan and M.Madheswaran, “Analysis and Simulation of LCL Series Resonant Full Bridge Converter Using PWM Technique with Load Independent Operation” has been presented in ICTES’08, a IEEE / IET International Conference organized by M.G.R.University, Chennai.Vol.no.1, pp.190-195, Dec.2007
20. Suganthi Mullainathan, Ramesh Natarajan, “An SPSS and CNN modelling based quality assessment using ceramic materials and membrane filtration techniques”, Revista Materia (Rio J.) Vol. 30, 2025, DOI: https://doi.org/10.1590/1517-7076-RMAT-2024-0721
21. M Suganthi, N Ramesh, “Treatment of water using natural zeolite as membrane filter”, Journal of Environmental Protection and Ecology, Volume 23, Issue 2, pp: 520-530,2022
22. R.A. Finkel and J.L. Bentley, “Quad Trees: A Data Structure for Retrieval on Composite Key,” Acta information, vol. 4, no. 1, pp. 1-9, 1974.
23. R. Tibshirani, G. Walther, and T. Hastie, “Estimating the Number of Clusters in a Dataset via the Gap Statistic,” J. Statistical Soc., vol. 63, no. 2, pp. 411-423, 2001.
24. Y. Jung, H. Park, and D.Z. Du, “A Decision Criterion for the Optimal Number of Clusters in Hierarchical Clustering,” J. Global Optimization, vol. 25, pp. 91-111, 2003.
25. D. Steinley and M.J. Brusco, “Initializing K-Means Batch Clustering: A Critical Evaluation of Several Techniques,” J. Classification, vol. 24, pp. 99- 121, 2007.
26. Likas, N. Vlassis, and J. Verbeek, “The Global K-means Clustering Algorithm,” Pattern Recognition, vol. 36, pp. 451-461, 2003.
27. Anand, L. (2023). An Intelligent AI and ML–Driven Cloud Security Framework for Financial Workflows and Wastewater Analytics. International Journal of Humanities and Information Technology, 5(02), 87-94.
28. Soundappan, S. J. (2020). Big Data Analytics in Healthcare: Applications for Pandemic Forecasting. International Journal of Advanced Research in Computer Science & Technology, 3(1), 2248-2253.
29. Rajasekar, M. (2024). Real-Time Predictive DevOps Intelligence for Risk-Aware Digital Business Processes in Cloud and SAP Ecosystems. International Journal of Advanced Research in Computer Science & Technology, 7(4), 10713-10718.
30. Poornima, G., & Anand, L. (2024, May). Novel AI Multimodal Approach for Combating Against Pulmonary Carcinoma. In 2024 5th International Conference for Emerging Technology (INCET) (pp. 1-6). IEEE.
31. Prabha, P. S., & Rengarajan, A. (2025). Adaptive Cloud Resource Allocation Using Attention-Driven Deep Reinforcement Learning. Engineering, Technology & Applied Science Research, 15(6), 29334-29340.
32. Jagadeesh, S., & Sugumar, R. (2017). A Comparative study on Artificial Bee Colony with modified ABC algorithm. European Journal of Applied Sciences, 9(5), 243-248.
33. Varma, K. K., & Anand, L. (2025, March). Deep Learning Driven Proactive Auto Scaler for High-Quality Cloud Services. In International Conference on Computing and Communication Systems for Industrial Applications (pp. 329-338). Singapore: Springer Nature Singapore.
34. Kumar, S. A., & Anand, L. (2025). A Novel EEG-Based Deep Learning Framework for Enhancing Communication in Locked-In Syndrome Using P300 Speller and Attention Mechanisms. KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, 19(11), 3841-3855.
35. Poornima, G., & Anand, L. (2025). Medical image fusion model using CT and MRI images based on dual scale weighted fusion based residual attention network with encoder-decoder architecture. Biomedical Signal Processing and Control, 108, 107932.
36. Archana, R., & Anand, L. (2025). Residual u-net with Self-Attention based deep convolutional adaptive capsule network for liver cancer segmentation and classification. Biomedical Signal Processing and Control, 105, 107665.Kumar, S. A., & Anand, L. (2025). A Novel EEG-Based Deep Learning Framework for Enhancing Communication in Locked-In Syndrome Using P300 Speller and Attention Mechanisms. KSII Transactions on Internet and Information Systems, 19(11), 3841-3855.
37. Rengarajan, A. (2025). Cloud-Based AI-Driven Threat Detection Framework for Smart Grid Cybersecurity. International Journal of Future Innovative Science and Technology, 8(6), 16065.
38. Murugeshwari, B., Sudharson, K., Panimalar, S. P., Shanmugapriya, M., & Abinaya, M. (2020). SAFE–Secure Authentication in Federated Environment using CEG Key code.
39. Raj A. A., & Sugumar, R. (2023). Early Detection of COVID-19 with Impact on Cardiovascular Complications using CNN Utilising Pre-Processed Chest X-Ray Images. 2023 International Conference on Applied Intelligence and Sustainable Computing (ICAISC), IEEE.
40. Jagadeesh, S., & Sugumar, R. (2017). A Comparative study on Artificial Bee Colony with modified ABC algorithm. European Journal of Applied Sciences, 9(5), 243-248.
41. Selvi, G. V., Anbarasan, A. B., Murthy, B. A., & Prabavathy, S. (2023). An Application Oriented Integrated Unequal Clustering Algorithm for Wireless Sensor Network. In Underwater Vehicle Control and Communication Systems Based on Machine Learning Techniques (pp. 140-154). CRC Press.
42. Sruthi, R. S., Ananya, S., & Murugeshwari, B. (2010). Web Based Virtual Control System Laboratory and On-Line Temperature Control of Electrophoresis Equipment using LabVIEW. International Journal of Computer Applications, 975, 8887.
