In recent years some research works have been carried out on web software error analysis and reliability predictions. In all these works the web environment has been considered as crisp one, which is not a very realistic assumption. Moreover, web error forecasting remains unworthy for the researchers for quite a long time. Furthermore, among various well known forecasting techniques, fuzzy time series based methods are extensively used, though they are suffering from some serious drawbacks, viz., fixed sized intervals, using some fixed membership values (0, 0.5, and 1) and moreover, the defuzzification process only deals with the factor that is to be predicted. Prompted by these facts, the present authors have proposed a novel multivariate fuzzy forecasting algorithm that is able to remove all the aforementioned drawbacks as also can predict the future occurrences of different web failures (considering the web environment as fuzzy) with better predictive accuracy. Also, the complexity analysis of the proposed algorithm is done to unveil its better run time complexity. Moreover, the comparisons with the other existing frequently used forecasting algorithms were performed to demonstrate its better efficiency and predictive accuracy. Additionally, at the very end, the developed algorithm was applied on the real web failure data of http://www.ismdhanbad.ac.in/, the official website of ISM Dhanbad, India, collected from the corresponding HTTP log files. cluster r which is generated after modulo − m operation. the ith (i =0,1…m −1) cluster or interval related to the main factor. the linguistic variables corresponding to a i . the ith cluster or interval of the jth secondary factor. the linguistic variables corresponding to b j,i . it is the largest element of a cluster a i (i =0,1…m −1). it is the smallest element of a cluster a i (i =0,1…m−1). it is the average of max_clust[a i ]and min_clust[a i ] of cluster a i . distance between any two data points a and b. the jth (j =1,2…n) element of the ith (i =0,1…m −1) cluster. the membership of e_main(i,k) on a n . number of elements of a k (k =0,1…m −1) whose distances from e_main(p,q) is greater than sum_deviation of the main factor. The qth element of the pth cluster of the ith secondary factor. number of elements of the kth cluster of the ith secondary factor whose distances from e_sec(i) p,q is greater than the sum_deviation of the ith=(i =1,2…k) secondary factor. the changed mid_clust of a ( m i d _ c l u s t [ i ] m o d m ) . largest element of a n . smallest element of a n . the predicted value of i. 
Web software fault prediction under fuzzy environment using MODULO-M multivariate overlapping fuzzy clustering algorithm and newly proposed revised prediction algorithm