The aim of development of models for software error prediction is to use measures that may be obtained relatively prior in the software development life cycle to provide reasonable initial estimates of quality of an evolving software system. Artificial intelligence technique is used for this, viz. ANFIS for the development of software defect prediction model is a very appropriate technique because predicting the defective modules in a software system prior to project deployment is a very important, because to a decrease in the total cost of the project and an increase in overall project success rate. Defect prediction will give one more chance to the development team to test again the modules or files for which the defectiveness probability is high. Here it was demonstrated that models developed using ANFIS technique could be used for solving this critical issue. It is clearly demonstrated that ANFIS can be trained to accurately predict software defect using variable process parameters. The interpretation results in chapter 4 demonstrate one of the real strengths of ANFIS is that they perform well even when the training data contains noise and measurement errors. That is, during learning, ANFIS are able to filter out noise and measurement error and effectively generalise the system behaviour. From the analysis of the results, given under heading “Results and Discussions”, Chapter-4, it is seen that the software defect prediction model developed using ANFIS technique has been able to perform well. The overall RMSE value obtained from ANFIS model is 8.164 for radius of influence r=0.75, which is almost half of that for r=0.50 and 1.0. Further from Fig. 3 & 4 and Table 6 it is seen that ANFIS mode>