A study of mining algorithms for finding accurate results and marking irregularities in software fault prediction

In the paper we are showing a comparative study of some of the classification and the clustering algorithms so that we can find the alternatives for the datasets depending upon the requirement. These prediction systems can be used for the software fault prediction as various algorithms in this paper have been categorized and simulation results shown. Prevalence informs the total case load at a given time. Incidence yields a pointer to extent of attention required and choice of measures. Different classification algorithms are trained on the result set to build the final classifier model based on K-fold cross validation method. The best accuracy for the given dataset is achieved in rotation forest algorithm compared to other classifiers. The proposed approach helps high level managers in their management decisions and also in their resource planning procedures for different categorical products.