Dynamic Detection of Software DEffects using Supervised Learning Techniques.

Alaa Al-Nusirat; Feras Hanandeh; Mohammad Kharabsheh; Mahmoud Al-Ayyoub; Nahla Al-dhufairi

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Dynamic Detection of Software DEffects using Supervised Learning Techniques.

Author(s)

Alaa Al-Nusirat Faculty of Prince Al-Hussein Bin Abdallah II For Information Technology, The Hashemite University, Jordan

Feras Hanandeh Faculty of Prince Al-Hussein Bin Abdallah II For Information Technology, The Hashemite University, Jordan

Mohammad Kharabsheh Faculty of Prince Al-Hussein Bin Abdallah II For Information Technology, The Hashemite University, Jordan

Mahmoud Al-Ayyoub Faculty of Computer and Information Technology, Jordan University of Science and Technology, Jordan

Nahla Al-dhufairi School of Engineering, Babylon University, Iraq

Abstract

In software testing, automatic detection of faults and defects in software is both complex and important. There are different techniques utilized to predict future defects. Machine learning is one of the most significant techniques used to build such prediction models. In this paper, we conduct a systematic review of the supervised machine learning techniques (classifiers) that are used for software defect prediction and evaluate their performance on several benchmark datasets. We experiment with different parameter values for the classifiers and explore the usefulness of employing dimensionality reduction techniques, such as Principle Component Analysis (PCA), and Ensemble Learning techniques. The results show the effectiveness of the considered classifiers in detecting bugs. Additionally, using PCA did not have a noticeable impact on prediction systems performance while parameter tuning positively impact classifies' accuracy, especially with Artificial Neural Network (ANN). The best results are obtained by using Ensemble Learning methods such as Bagging (which achieves 95.1% accuracy with Mozilla dataset) and Voting (which achieves 93.79% accuracy with kc1 dataset).

Publication Details

Page(s) 185-191

DOI DOI not available

Published Journal: International Journal of Communication Networks and Information Security, Volume: 11, Issue: 1, Year: 2019

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Keywords

Software defects prediction

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