Combining integreted sampling technique with feature selection for software defect prediction

Good quality software is a supporting factor that is important in any line of work in of society. But the software component defective or damaged resulting in reduced performance of the work, and can increase the cost of development and maintenance. An accurate prediction on software module prone defects as part of efforts to reduce the increasing cost of development and maintenance of software. An accurate prediction on software module prone defects as part of efforts to reduce the increasing cost of development and maintenance of software. From the results of these studies are known, there are two problems that can decrease performance prediction of classifiers such imbalances in the distribution of the class and irrelevant of the attributes that exist in the dataset. So as to handle both of these issues, we conducted this research using integrated a sample technique with feature selection method. Based on research done previously, there are two methods of samples including random under sampling and SMOTE for random over sampling. While on feature selection method such as chi square, information gain and relief methods. After doing the research process, integration SMOTE technique with relief method used on Naïve Bayes classifiers, the result of the predicted value better than any other method that is 82%.