Software maintainability prediction of open source datasets using least squares support vector machines

References

• Baskar, N. and Chandrasekar, C., 2018. An Evolving Neuro-PSO-based Software Maintainability Prediction.
   Google Scholar
• Baylar, A., Hanbay, D., and Batan, M., 2009. Application of least square support vector machines in the prediction of aeration performance of plunging overfall jets from weirs. Expert Systems with Applications, 36 (4), 8368–8374. doi: 10.1016/j.eswa.2008.10.061
   Web of Science ®Google Scholar
• Chug, A. and Malhotra, R., 2016. Benchmarking framework for maintainability prediction of open source software using object oriented metrics. International Journal of Innovative Computing, Information and Control, 12 (2), 615–634.
   Web of Science ®Google Scholar
• Conte S. D., Dunsmore H. E., and S.V.Y., 1986. Software Engineering Metrics and Models. Benjamin-Cummings Publishing Co., Inc. Redwood City, CA, USA.
   Google Scholar
• Elish, M.O. and Elish, K.O., 2009. Application of treenet in predicting object-oriented software maintainability: A comparative study. In: 2009 13th European Conference on Software Maintenance and Reengineering. 69–78.
   Google Scholar
• Gupta, S. and Chug, A., 2020a. Assessing Cross-Project Technique for Software Maintainability Prediction. Procedia Computer Science, 167, 656–665. doi: 10.1016/j.procs.2020.03.332
   Google Scholar
• Gupta, S. and Chug, A., 2020b. Software maintainability prediction using an enhanced random forest algorithm. Journal of Discrete Mathematical Sciences and Cryptography, 23 (2), 441–449. doi: 10.1080/09720529.2020.1728898
   Web of Science ®Google Scholar
• Houston, D. and Bert Keats, J., 1998. Cost of software quality: a means of promoting software process improvement. Quality Engineering, 10 (3), 563–573. doi: 10.1080/08982119808919170
   Google Scholar
• IEEE Standard for Software Maintenance, 1993. IEEE Std 1219-1993.
   Google Scholar
• Issac, G., Rajendran, C., and Anantharaman, R.N., 2003. Determinants of software quality: customer’s perspective. Total Quality Management & Business Excellence, 14 (9), 1053–1070. doi: 10.1080/1478336032000090950
   Web of Science ®Google Scholar
• Jatain, A., 2020. Metric based reusability analysis of software systems. Journal of Interdisciplinary Mathematics, 23 (1), 107–116. doi: 10.1080/09720502.2020.1721672
   Web of Science ®Google Scholar
• Kaur, A. and Kaur, K., 2013. Statistical comparison of modelling methods for software maintainability prediction. International Journal of Software Engineering and Knowledge Engineering, 23 (6), 743–774. doi: 10.1142/S0218194013500198
   Web of Science ®Google Scholar
• Kaur, S. and Sagar, B.B., 2019. Brahmi character recognition based on SVM (support vector machine) classifier using image gradient features. Journal of Discrete Mathematical Sciences and Cryptography, 22 (8), 1365–1381. doi: 10.1080/09720529.2019.1692445
   Web of Science ®Google Scholar
• Li, S.-H., Yen, D.C., Lu, W.-H., and Chen, T.-Y., 2014. The characteristics of information system maintenance: an empirical analysis. Total Quality Management & Business Excellence, 25 (3–4), 280–295. doi: 10.1080/14783363.2013.807679
   Web of Science ®Google Scholar
• Li, W. and Henry, S., 1993. Object-oriented metrics that predict maintainability. Journal of systems and software, 23 (2), 111–122. doi: 10.1016/0164-1212(93)90077-B
   Web of Science ®Google Scholar
• Malhotra, R. and Chug, A., 2014. Application of evolutionary algorithms for software maintainability prediction using object-oriented metrics. In: Proceedings of the 8th International Conference on Bioinspired Information and Communications Technologies. 348–351.
   Google Scholar
• Malhotra, R. and Chug, A., 2016. Software Maintainability: Systematic Literature Review and Current Trends. International Journal of Software Engineering and Knowledge Engineering, 26 (8), 1221–1253. doi: 10.1142/S0218194016500431
   Web of Science ®Google Scholar
• Malhotra, R. and Chug, A., 2012. Software Maintainability Prediction using Machine Learning Algorithms. Software engineering: an international Journal (SeiJ), 2 (2), 19–36.
   Google Scholar
• Patro, S. and Sahu, K.K., 2015. Normalization: A preprocessing stage. arXiv preprint arXiv:1503.06462.
   Google Scholar
• Saini, G.L., Panwar, D., Kumar, S., and Singh, V., 2020. A systematic literature review and comparative study of different software quality models. Journal of Discrete Mathematical Sciences and Cryptography, 23 (2), 585–593. doi: 10.1080/09720529.2020.1747188
   Web of Science ®Google Scholar
• Samui, P. and Sitharam, T.G., 2009. Application of least squares support vector machine in seismic attenuation prediction. ISET Journal of Earthquake Technology, 46 (3–4), 147–155.
   Google Scholar
• Sharma, D. and Chandra, P., 2020. Linear regression with factor analysis in fault prediction of software. Journal of Interdisciplinary Mathematics, 23 (1), 11–19. doi: 10.1080/09720502.2020.1721641
   Web of Science ®Google Scholar
• Suykens, J.A.K., De Brabanter, J., Lukas, L., and Vandewalle, J., 2002. Weighted least squares support vector machines: robustness and sparse approximation. Neurocomputing, 48 (1–4), 85–105. doi: 10.1016/S0925-2312(01)00644-0
   Web of Science ®Google Scholar
• Suykens, J.A.K. and Vandewalle, J., 1999. Least squares support vector machine classifiers. Neural processing letters, 9 (3), 293–300. doi: 10.1023/A:1018628609742
   Web of Science ®Google Scholar
• Thwin, M.M.T. and Quah, T.-S., 2005. Application of neural networks for software quality prediction using object-oriented metrics. Journal of systems and software, 76 (2), 147–156. doi: 10.1016/j.jss.2004.05.001
   Web of Science ®Google Scholar