Qasim Ali1, Asma Zubedi2, Fatima Najmuddin1*, Imran Memon3 and Salahuddin Sadar1
1Mehran University of Engineering and Technology, Jamshoro, Software Department, Pakistan
2Beijing University of Post and Telecommunication, Management and Beijing, China
3Bahria University Karachi Campus, Computer Science Department, Karachi, Pakistan
*Corresponding Author: Fatima Najmuddin, Mehran University of Engineering and Technology, Jamshoro, Software Department, Pakistan.
Received: March 22, 2023; Published: March 29, 2023
The dependency on software has been increasing with each passing day, due to which reliability and quality of software has been becoming more and more crucial. The quality of product increases when the defects and faults will decrease. To find the defect in software product, many approaches were proposed but machine learning approach is very useful. Machine learning classify data into defective and non-defective modules.
In this paper, 15 datasets from NASA promise repository named AR1, AR3, AR5, AR6, CM1, KC1, KC2, KC3, MC1, MC2, MW1, PC1, PC2, PC3, PC4 are analyzed using 7 machine learning algorithms (K-Nearest Neighbor, Linear regression, Random Forest, Naïve Bayes, Support Vector Machine (SVM), logistic regression and decision tree) and apply 10 k-fold cross validation in RapidMiner tool. In RapidMiner performance of ML algorithm in term of accuracy is calculated and the summary of the result shows that SVM perform best.
Similarly, these 15 datasets are also analyzed using 8 machine learning algorithms named Random Forest, Naïve Bayes, simple logistic, Sequential minimal optimization (SMO), K-star, REF (decision tree), K-Nearest Neighbor (KNN), decision table and apply 10 k-fold cross validation in WEKA simulation tool. In WEKA performance of ML algorithm in term of correctly classified instances is calculated and the summary of the result shows that Random Forest and simple logistic perform best.
Keywords: Software Defect; Machine Learning Algorithms; Weka; RapidMiner; Nasa Promise Datasets
Citation: Fatima Najmuddin., et al. “Comparative Study: To Analyze Software Defects Using Machine Learning Algorithms". Acta Scientific Computer Sciences 5.4 (2023): 103-108.
Copyright: © 2023 Fatima Najmuddin., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.