Study of Data Mining Algorithms Using a Dataset from theSize-Effect on Open Source Software Defects

This article focuses on the quality of data mining algorithms in terms of the accuracyratio and time consumption. so, in order to figure out the best algorithm among theclassification and clustering algorithms, the weka program will be testing all algorithmsusing a real dataset from the size effect on defect proneness for open source software. themozilla product is adopted as an example of open source software. the dataset that is used inthis paper represents the output of the study of the size effect on defect proneness in the opensource software. the study of mozilla product shows a significant relationship between thesize of software and the number of defect proneness in software. the mozilla product studyproduced a dataset to be as inputs of the weka program in order to compare the data miningtools (algorithms). we use the naive bayes, decision trees j48, expectation-maximizationfor classifying and k-star and simple kmeans for clustering methods. the findingsdemonstrate the difference between the algorithms according to the accuracy, and the timeconsuming to reach the result in each algorithm. furthermore, the effect of the software size issignificant on defect proneness. finally, the experiments are conducted in weka with theaim of this research is finding out the best algorithm in terms of accuracy and timeconsuming.at the end, the paper will be figuring out the best algorithm in order to chooseand depending on it in the tests of classification and clustering