Analysis of Software Fault and Defect Prediction by Fuzzy C-Means Clustering and Adaptive Neuro Fuzzy C-Means Clustering

Faults are related to failures and they do not have much power for indicating a higher quality or a better system above the baseline that the end-users expect.The system faults are the defects that brim in executable files. Conventional approaches employ the experts to navigate directly into the source code errors. However expansion in system size grew the complexity of task exponentially and generated a scope for new methods in fault classification.Experimental studies have shown that miniature bugs are reason of faults. In a considerable size of system the faulty labels and non-faulty labels are marked during modular phase. This paper presents the adaptiveneuro fuzzy c-means clustering for fault classification via fuzzy c-means clustering.Experimental studies confirmed that only a small portion of software modules cause faults in software systems.The NASA pc1 database is used for experiments and the results in this approach is enhanced than previous clustering based approaches.