Please use this identifier to cite or link to this item: http://dspace.dtu.ac.in:8080/jspui/handle/repository/18851
Title: AN IMPROVED CNN BASED ARCHITECTURE FOR WITHIN-PROJECT SOFTWARE DEFECT PREDICTION
Authors: YADAV, HITENDRA SINGH
Keywords: MATTHEWS CORRELATION COEFFICIENT (MCC)
SVM
F AND G MEASURE
CNN
Issue Date: Jun-2020
Publisher: DELHI TECHNOLOGICAL UNIVERSITY
Series/Report no.: TD - 5385;
Abstract: To improve the software quality, the software is generally tested to find out any bugs or a simple reliability test. A reliable software defect checking mechanism is a leading research topic, in the era of dependency on software’s for several tasks. Many researchers used different techniques of deep learning algorithm such as CNN i.e convolutional neural networks, and deep belief networks for prediction of software defect. these algorithms failed to provide higher prediction accuracies. To overcome the issues a new algorithm for software defect prediction is required for higher accuracy and other parameters like F- and G-measure and specifically important parameter is Matthews correlation coefficient (MCC) measure. In this paper, a new modified CNN algorithm is proposed, which combines the CNN based models into one and apply concatenate algorithm under SVM i.e. support vector machine classifier. The results clearly indicate that the proposed algorithm improves the parameters and thus is a highly dependable and reliable method for software defect prediction.
URI: http://dspace.dtu.ac.in:8080/jspui/handle/repository/18851
Appears in Collections:M.E./M.Tech. Computer Engineering

Files in This Item:
File Description SizeFormat 
Hitendra Thesis.pdf1.49 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.