COMPREHENSIVE STUDY OF SOFTWARE DEFECT PREDICTION USING DATA MINING

Software Defect Prediction is the area that is used to predict the defects before they occur. Nowa-days more reliable and accurate software’s are produced for the users. Performance is compulsory for the software’s and mostly depends upon the accuracy because software’s should be worked accurately when they are delivered to end user. This means how much a software is best with respect to accuracy. In this research different algorithms are used to analyze the performance with respect to accuracy for the software defect prediction. The performance based accuracy of a software can be boosted up by predicting the faults. Hence the software fault prediction is utilized to produce very accurate and less costly new software’s. For, this purpose classification based analysis is used in Weka to implement the data analysis. The data is collected from the open source and publically available data set like i.e promise data repository. In this paper the comparative comprehensive analysis are used and how these analysis shows the results regarding selecting the best algorithm for predicting the software faults.