Performance Analysis of Classification Algorithms for Software Defects Prediction by Mathematical Modelling & Simulations
Abstract
This study explores machine learning (ML) techniques for Software defects prediction (SDP) by using
Mathematical Modelling & Simulation. The SDP is also used in the critical systems of aviation, healthcare, manufacturing,
and robotics. Many organizations face difficulty in forecasting the accurate defect before software deployment which is
actually very crucial for estimating delivery time, maintenance efforts, and ensuring quality expectations. SDP enhances
software quality by spotting potential defects in the upkeep phase. The current models of SDP rely on static program metrics
for machine learning classifiers, but manual feature engineering may miss vital information impacting defect prediction
accuracy. This study initially explores the past SDP results then aims to develop methods by adapting to future anomaly
detection techniques. The study explores the various approaches of SDP which include K-Means methodology, Support
Vector Machines (SVM) linear, Random Forest (RF) & Multi-layer Perceptron (MLP) algorithms and discussed the current
models of SDP. The proposed SDP models are rigorously evaluated by using metrics like false alarm rate, precision, and
detection rate. The results show high accuracy for K-Means and MLP (99.67%), K-Means and SVML (99.19%), and KMeans and RF (97.76%) for defect prediction.
References
650, 1995.
[2] K. S. Kavya and D. Y. Prasanth, “An ensemble deepboost classifier for software defect prediction,” Int. J. Adv. Trends Comput.
Sci. Eng., vol. 9, no. 2, pp. 2021–2028, 2020.
[3] R. B. Jadhav, S. D. Joshi, U. G. Thorat, and A. S. Joshi, “A software defect learning and analysis utilizing regression method for
quality software development,” Int. J. Adv. Trends Comput. Sci. Eng., vol. 8, no. 4, pp. 1275–1282, 2019.
[4] M. K. Albzeirat, M. I. Hussain, R. Ahmad, F. M. Al-Saraireh, and I. Ahmad, “A novel mathematical logic for improvement using
lean manufacturing practices,” J. Adv. Manuf. Syst., vol. 17, no. 03, pp. 391–413, 2018.
[5] A. G. Liu, E. Musial, and M.-H. Chen, “Progressive reliability forecasting of service-oriented software,” in 2011 IEEE international
conference on web services, IEEE, 2011, pp. 532–539.
[6] T. Menzies, Z. Milton, B. Turhan, B. Cukic, Y. Jiang, and A. Bener, “Defect prediction from static code features: current results,
limitations, new approaches,” Autom. Softw. Eng., vol. 17, pp. 375–407, 2010.
[7] E. Erturk and E. A. Sezer, “A comparison of some soft computing methods for software fault prediction,” Expert Syst. Appl., vol.
42, no. 4, pp. 1872–1879, 2015.
[8] M. K. Albzeirat, M. I. Hussain, R. Ahmad, F. M. Al-Saraireh, A. Salahuddin, and N. Bin-Abdun, “Applications of nano-fluid in
nuclear power plants within a future vision,” Int. J. Appl. Eng. Res., vol. 13, no. 7, pp. 5528–5533, 2018.
[9] S. Lessmann, B. Baesens, C. Mues, and S. Pietsch, “Benchmarking classification models for software defect prediction: A proposed
framework and novel findings,” IEEE Trans. Softw. Eng., vol. 34, no. 4, pp. 485–496, 2008.
[10] M. Singh and D. S. Salaria, “Software defect prediction tool based on neural network,” Int. J. Comput. Appl., vol. 70, no. 22, 2013.
