Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2013, Article ID 312067, 21 pages
http://dx.doi.org/10.1155/2013/312067
Research Article

Empirical Study of Homogeneous and Heterogeneous Ensemble Models for Software Development Effort Estimation

1Information and Computer Science Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
2College of Engineering, Tanta University, Tanta, Egypt

Received 13 March 2013; Revised 3 June 2013; Accepted 9 June 2013

Academic Editor: Ren-Jieh Kuo

Copyright © 2013 Mahmoud O. Elish et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [13 citations]

The following is the list of published articles that have cited the current article.

  • Judith Neugebauer, Jorg Bremer, Christian Hinrichs, Oliver Kramer, and Michael Sonnenschein, “Generalized cascade classification model with customized transformation based ensembles,” 2016 International Joint Conference on Neural Networks (IJCNN), pp. 4056–4063, . View at Publisher · View at Google Scholar
  • Mohamed Hosni, Ali Idri, Ali Bou Nassif, and Alain Abran, “Heterogeneous Ensembles for Software Development Effort Estimation,” 2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI), pp. 174–178, . View at Publisher · View at Google Scholar
  • Sousuke Amasaki, “A Comparative Study on Linear Combination Rules for Ensemble Effort Estimation,” 2017 43rd Euromicro Conference on Software Engineering and Advanced Applications (SEAA), pp. 104–107, . View at Publisher · View at Google Scholar
  • Unknown, Mohamed Hosni, and Ali Idri, “Software effort estimation using classical analogy ensembles based on random subspace,” Proceedings of the Symposium on Applied Computing - SAC '17, pp. 1251–1258, . View at Publisher · View at Google Scholar
  • Unknown, Sara Adel El-Shorbagy, Wael Mohamed El-Gammal, and Walid M. Abdelmoez, “Using SMOTE and Heterogeneous Stacking in Ensemble learning for Software Defect Prediction,” Proceedings of the 7th International Conference on Software and Information Engineering - ICSIE '18, pp. 44–47, . View at Publisher · View at Google Scholar
  • Mahmoud O. Elish, Hamoud Aljamaan, and Irfan Ahmad, “Three empirical studies on predicting software maintainability using ensemble methods,” Soft Computing, 2015. View at Publisher · View at Google Scholar
  • Neha Saini, and Goldie Gabrani, “Effort estimation models using evolutionary learning algorithms for software development,” 2016 Symposium on Colossal Data Analysis and Networking, CDAN 2016, 2016. View at Publisher · View at Google Scholar
  • Ali Idri, Mohamed Hosni, and Alain Abran, “Improved Estimation of Software Development Effort Using Classical and Fuzzy Analogy Ensembles,” Applied Soft Computing, 2016. View at Publisher · View at Google Scholar
  • Ali Idri, Mohamed Hosni, and Alain Abran, “Systematic Literature Review of Ensemble Effort Estimation,” Journal of Systems and Software, 2016. View at Publisher · View at Google Scholar
  • Mohamed Hosni, Ali Idri, Alain Abran, and Ali Bou Nassif, “On the value of parameter tuning in heterogeneous ensembles effort estimation,” Soft Computing, vol. 22, no. 18, pp. 5977–6010, 2017. View at Publisher · View at Google Scholar
  • Ali Idri, Mohamed Hosni, and Alain Abran, “Investigating heterogeneous ensembles with filter feature selection for software effort estimation,” ACM International Conference Proceeding Series, vol. 131936, pp. 207–220, 2017. View at Publisher · View at Google Scholar
  • P. Suresh Kumar, and H. S. Behera, “Role of Soft Computing Techniques in Software Effort Estimation: An Analytical Study,” Computational Intelligence in Pattern Recognition, vol. 999, pp. 807–831, 2019. View at Publisher · View at Google Scholar
  • Zakrani Abdelali, Moutachaouik Hicham, and Namir Abdelwahed, “An Ensemble of Optimal Trees for Software Development Effort Estimation,” Xenopus, vol. 1865, pp. 55–68, 2019. View at Publisher · View at Google Scholar