This paper is published in Volume-5, Issue-2, 2019
Area
Journal
Author
Sree Abirami S.
Co-authors
Umavathi R., Benula L., Mathiyalagan P.
Org/Univ
Sri Ramakrishna Engineering College, Coimbatore, Tamil Nadu, India
Pub. Date
04 April, 2019
Paper ID
V5I2-1738
Publisher
Keywords
Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Kernel PCA

Citationsacebook

IEEE
Sree Abirami S., Umavathi R., Benula L., Mathiyalagan P.. Software defect prediction, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Sree Abirami S., Umavathi R., Benula L., Mathiyalagan P. (2019). Software defect prediction. International Journal of Advance Research, Ideas and Innovations in Technology, 5(2) www.IJARIIT.com.

MLA
Sree Abirami S., Umavathi R., Benula L., Mathiyalagan P.. "Software defect prediction." International Journal of Advance Research, Ideas and Innovations in Technology 5.2 (2019). www.IJARIIT.com.

Abstract

Software Defect Prediction is an important aspect in order to ensure software quality. Deep Learning techniques can also be used for the same. In this paper, we propose to extract a set of expressive features from an initial set of basic change measures using Artificial Neural Network (ANN), and then train a classifier based on the extracted features using Decision tree and compare it to three other methods wherein features are extracted from a set of initial change measures using dimensionality reduction techniques that include Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Kernel PCA. We use five open source datasets from NASA Promise Data Repository to perform this comparative study. For evaluation, three widely used metrics: Accuracy, F1 scores and Areas under Receiver Operating Characteristic curve are used. It is found that the Artificial Neural Network outperformed all the other dimensionality reduction techniques. Kernel PCA performed best amongst the dimensionality reduction techniques.