earticle

영어

Unsupervised learning techniques such as clustering can be used in software fault prediction, where fault labels are not available. The accurate prediction of faults is likely to occur in coding and that can be rectified early testing phase, which reduces the testing cost as well as maintenance cost and enhance the quality of software. In respect of data mining approach, if training data are not present, then I can not use the supervised learning, this is the biggest problem. To solve this problem, new models using unsupervised learning such as clustering algorithms are quite necessary. This work is the extended version of a Various Strategies and Technical Aspects of Data Mining: A Theoretical Approach, which moves towards practical implementation from theoretical foundation [1]. The main objective of this work to find whether software is faulty or non faulty by using the confusion matrix and also calculating the False Positive Rate (FPR), False Negative Rate (FNR), and Error or fault in a software module. In order to obtain the results I have used an indigenous tool.