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Research Article

Multi-Step Dynamic Ensemble Selection to Estimate Software Effort

, , & ORCID Icon
Article: 2351718 | Received 18 Mar 2024, Accepted 26 Apr 2024, Published online: 16 May 2024

References

  • Alhazmi, O. H., and M. Zubair Khan. 2020. Software effort prediction using ensemble learning methods. Journal of Software Engineering and Applications 13 (7):143–20. doi:10.4236/jsea.2020.137010
  • Arau´jo, R. D. A., S. Soares, and A. L. Oliveira. 2012. Hybrid morphological methodology for software development cost estimation. Expert Systems with Applications 39 (6):6129–39. doi:10.1016/j.eswa.2011.11.077
  • Bardsiri, V. K., D. Norhayati Abang Jawawi, A. Khatibi Bardsiri, and E. Khatibi. 2013. LMES: A localized multi-estimator model to estimate software development effort. Engineering Applications of Artificial Intelligence 26 (10):2624–40. doi:10.1016/j.engappai.2013.08.005
  • Britto, A. S., Jr, R. Sabourin, and L. E. Oliveira. 2014. Dynamic selection of classifiers—a comprehensive review. Pattern Recognition 47 (11):3665–80. doi:10.1016/j.patcog.2014.05.003
  • Brownlee, J. 2017. Difference between classification and regression in machine learning. Machine Learning Mastery 25:985–1.
  • Brownlee, J. 2019. How to choose a feature selection method for machine learning. Machine Learning Mastery 10:1–7.
  • Burgess, C. J., and M. Lefley. 2001. Can genetic programming improve software effort estimation? A comparative evaluation. Information and Software Technology 43 (14):863–73. doi:10.1016/S0950-5849(01)00192-6
  • Cabral, J. T. H. D. A., and A. L. Oliveira. 2021. Ensemble effort estimation using dynamic selection. Journal of Systems and Software 175:110904. doi:10.1016/j.jss.2021.110904
  • Cruz, R. M., L. G. Hafemann, R. Sabourin, and G. D. Cavalcanti. 2020. DESlib: A dynamic ensemble selection library in Python. Journal of Machine Learning Research 21 (8):1–5. doi:10.1214/10-BA521
  • Desharnais, J. M. 1989. Analyse statistique de la productivitie des projects informatique a partie de la technique des point des function. Masters thesis University of Montreal.
  • Dong, X., Z. Yu, W. Cao, Y. Shi, and Q. Ma. 2020. A survey on ensemble learning. Frontiers of Computer Science 14 (2):241–58. doi:10.1007/s11704-019-8208-z
  • Garc´ıa, S., Z. Zhong-Liang, A. Abdulrahman, A. Saleh, and H. Francisco. 2018. Dynamic ensemble selection for multi-class imbalanced datasets. Information Sciences 445:22–37. doi:10.1016/j.ins.2018.03.002
  • Hidmi, O., and B. Erdogdu Sakar. 2017. Software development effort estimation using ensemble machine learning. International Journal of Computing, Communication and Instrumentation Engineering 4 (1):143–47.
  • Hosni, M., A. Idri, and A. Abran. 2017. “Investigating heterogeneous ensembles with filter feature selection for software effort estimation.” In Proceedings of the 27th international workshop on software measurement and 12th international conference on software process and product measurement, October 25–27, Gothenburg Sweden, 207–220.
  • Hosni, M., A. Idri, and A. Abran. 2021. On the value of filter feature selection techniques in homogeneous ensembles effort estimation. Journal of Software: Evolution and Process 33 (6):e2343. doi:10.1002/smr.2343
  • Idri, A., M. Hosni, and A. Abran. 2016. Systematic literature review of ensemble effort estimation. Journal of Systems and Software 118:151–75. doi:10.1016/j.jss.2016.05.016
  • Jorgensen, M., and M. Shepperd. 2006. A systematic review of software development cost estimation studies. IEEE Transactions on Software Engineering 33 (1):33–53. doi:10.1109/TSE.2007.256943
  • Kitchenham, B., S. Lawrence Pfleeger, B. McColl, and S. Eagan. 2002. An empirical study of maintenance and development estimation accuracy. Journal of Systems and Software 64 (1):57–77. doi:10.1016/S0164-1212(02)00021-3
  • Kocaguneli, E., T. Menzies, and J. W. Keung. 2011. On the value of ensemble effort estimation. IEEE Transactions on Software Engineering 38 (6):1403–16. doi:10.1109/TSE.2011.111
  • Li, Y. 2009. “Effort estimation: Maxwell.” Mar. doi:10.5281/zenodo.268461
  • Mahesh, B. 2020. Machine learning algorithms-A review. International Journal of Science and Research (Ijsr) [Internet] 9:381–86.
  • Mahmood, Y., N. Kama, A. Azmi, A. Salman Khan, and M. Ali. 2022. Software effort estimation accuracy prediction of machine learning techniques: A systematic performance evaluation. Software: Practice and Experience 52 (1):39–65. doi:10.1002/spe.3009
  • Mair, C., G. Kadoda, M. Lefley, K. Phalp, C. Schofield, M. Shepperd, and S. Webster. 2000. An investigation of machine learning based prediction systems. Journal of Systems and Software 53 (1):23–29. doi:10.1016/S0164-1212(00)00005-4
  • Malhotra, R., and A. Jain. 2011. Software effort prediction using statistical and machine learning methods. International Journal of Advanced Computer Science and Applications 2 (1). doi:10.14569/IJACSA.2011.020122
  • Marco, R., S. Sharifah Syed Ahmad, and S. Ahmad. 2022. Bayesian hyperparameter optimization and ensemble learning for machine learning models on software effort estimation. International Journal of Advanced Computer Science and Applications 13 (3). doi:10.14569/IJACSA.2022.0130351
  • Maxwell, K. D. 2002. Applied Statistics for Software Managers, Illustrated. 333. Prentice Hall PTR. https://books.google.co.in/books?id=irVQAAAAMAAJ
  • Mendes-Moreira, J., C. Soares, A. M. Jorge, and J. F. S. Sousa. 2012. Ensemble approaches for regression: A survey. ACM Computing Surveys (CSUR) 45 (1):1–40. doi:10.1145/2379776.2379786
  • Mousavi, R., M. Eftekhari, and F. Rahdari. 2018. Omni-ensemble learning (OEL): Utilizing over-bagging, static and dynamic ensemble selection approaches for software defect prediction. International Journal on Artificial Intelligence Tools 27 (6):1850024.
  • Pedregosa F., G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel,P. Prettenhofer, R. Weiss, V. Dubourg, et al. 2011. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12:2825–30.
  • Pospieszny, P., B. Czarnacka-Chrobot, and A. Kobylinski. 2018. An effective approach for software project effort and duration estimation with machine learning algorithms. Journal of Systems and Software 137:184–96. doi:10.1016/j.jss.2017.11.066
  • Radlinski, L., and W. Hoffmann. 2010. On predicting software development effort using machine learning techniques and local data. International Journal of Software Engineering and Computing 2 (2):123–36.
  • Rao, K. E., and G. Appa Rao. 2021. RETRACTED ARTICLE: Ensemble learning with recursive feature elimination integrated software effort estimation: A novel approach. Evolutionary Intelligence 14 (1):151–62. doi:10.1007/s12065-020-00360-5
  • Ray, S. 2019. “A quick review of machine learning algorithms.” In 2019 International conference on machine learning, big data, cloud and parallel computing (COMITCon), February 14–16, India, 35–39. IEEE.
  • Rehman, Israr Ur, Zulfiqar Ali, and Zahoor Jan. 2021. An empirical analysis on software development efforts estimation in machine learning perspective. Advances in Distributed Computing and Artificial Intelligence Journal.
  • Shepperd, M., and G. Kadoda. 2001. Comparing software prediction techniques using simulation. IEEE Transactions on Software Engineering 27 (11):1014–22. doi:10.1109/32.965341
  • Shukla, S., and S. Kumar. 2019. “Applicability of neural network based models for software effort estimation.” In 2019 IEEE World Congress on Services (SERVICES), Milan, Italy, Vol. 2642, 339–42. IEEE.
  • Suresh Kumar, P., H. S. Behera, J. Nayak, and B. Naik. 2022. A pragmatic ensemble learning approach for effective software effort estimation. Innovations in Systems and Software Engineering 18 (2):283–99. doi:10.1007/s11334-020-00379-y
  • Tsunoda, M. 2017. Kitchenham. doi:10.5281/zenodo.268457
  • Usman, M., E. Mendes, F. Weidt, and R. Britto. 2014. “Effort estimation in agile software development: A systematic literature review.” In Proceedings of the 10th international conference on predictive models in software engineering, Turin, Italy, 82–91.
  • Wen, J., S. Li, Z. Lin, Y. Hu, and C. Huang. 2012. Systematic literature review of machine learning based software development effort estimation models. Information and Software Technology 54 (1):41–59. doi:10.1016/j.infsof.2011.09.002
  • Yun, F. H. April 2010. China: Effort estimation dataset. April. doi:10.5281/zenodo.268446