Systematic Mapping Study of Ensemble Effort Estimation

Ali Idri, Mohamed Hosni, Alain Abran

Abstract

Ensemble methods have been used recently for prediction in data mining area in order to overcome the weaknesses of single estimation techniques. This approach consists on combining more than one single technique to predict a dependent variable and has attracted the attention of the software development effort estimation (SDEE) community. An ensemble effort estimation (EEE) technique combines several existing single/classical models. In this study, a systematic mapping study was carried out to identify the papers based on EEE techniques published in the period 2000-2015 and classified them according to five classification criteria: research type, research approach, EEE type, single models used to construct EEE techniques, and rule used the combine single estimates into an EEE technique. Publication channels and trends were also identified. Within the 16 studies selected, homogeneous EEE techniques were the most investigated. Furthermore, the machine learning single models were the most frequently employed to construct EEE techniques and two types of combiner (linear and non-linear) have been used to get the prediction value of an ensemble.

References

  1. Azhar, D. et al., 2013. Using Ensembles for Web Effort Estimation. In 2013 ACM / IEEE International Symposium on Empirical Software Engineering and Measurement. pp. 173-182. S10*
  2. Azzeh, M., Nassif, A.B. & Minku, L.L., 2015. An empirical evaluation of ensemble adjustment methods for analogy-based effort estimation. The Journal of Systems and Software, 103, pp.36-52. S15*
  3. Braga, P. et al., 2007. Bagging Predictors for Estimation of Software Project Effort. In Proceedings of International Joint Conference on Neural Networks. pp. 14-19. S1*
  4. Elish, M.O., 2013. Assessment of voting ensemble for estimating software development effort. In IEEE Symposium on Computational Intelligence and Data Mining (CIDM). Singapore, pp. 316-321. S5*
  5. Elish, M.O., 2009. Improved estimation of software project effort using multiple additive regression trees. Expert Systems with Applications, 36(7), pp.10774-10778. S7*
  6. Elish, M.O., Helmy, T. & Hussain, M.I., 2013. Empirical Study of Homogeneous and Heterogeneous Ensemble Models for Software Development Effort Estimation. Mathematical Problems in Engineering, 2013. S8*
  7. Hastie, T., Friedman, J. & Tibshirani, R., 2009. The Elements of Statistical Learning: Data Mining , Inference and Prediction Second Edi., Springer New York.
  8. Hsu, C.-J. et al., 2010. A Study of Improving the Accuracy of Software Effort Estimation Using Linearly Weighted Combinations. In Proceedings of the 34th IEEE Annual Computer Software and Applications Conference Workshops. Seoul, pp. 98-103. S6*
  9. Idri, A. & Abran, A., 2001. A Fuzzy Logic Based Set of Measures for Software Project Similarity: Validation and Possible Improvements. In Proceedings of the Seventh International Software Metrics Symposium. London, pp. 85 - 96.
  10. Idri, A., Amazal, F.A. & Abran, A., 2015. Analogy-based software development effort estimation: A systematic mapping and review. Information and Software Technology, 58, pp.206-230.
  11. Idri, A., Khoshgoftaar, T.M. & Abran, A., 2002. Investigating soft computing in case-based reasoning for software cost estimation. Engineering Intelligent Systems for Electrical Engineering and Communications, 10(3), pp.147-157.
  12. Idri, A., Zahi, A. & Abran, A., 2006. Software Cost Estimation by Fuzzy Analogy for Web Hypermedia Applications. In Proceedings of International Conference on Software Process and Product Measurement. Cadiz, Spain, pp. 53-62.
  13. Jorgensen, M. & Shepperd, M., 2007. A Systematic Review of Software Development Cost Estimation Studies. IEEE Transactions on Software Engineering, 33(1), pp.33-53.
  14. Kitchenham, B. & Charters, S., 2007. Guidelines for performing Systematic Literature Reviews in Software Engineering. Engineering, 2, p.1051.
  15. Kocaguneli, E., Kultur, Y. & Bener, A.B., 2009. Combining Multiple Learners Induced on Multiple Datasets for Software Effort Prediction. In Proceedings of International Symposium on Software Reliability Engineering. S2*
  16. Kocaguneli, E., Menzies, T. & Keung, J.W., 2012. On the Value of Ensemble Effort Estimation. IEEE Transactions on Software Engineering, 38(6), pp.1403-1416. S9*
  17. Kultur, Y., Turhan, B. & Bener, A., 2009. Ensemble of neural networks with associative memory (ENNA) for estimating software development costs. KnowledgeBased Systems, 22(6), pp.395-402. S3*
  18. Minku, L.L. & Yao, X., 2013a. An analysis of multiobjective evolutionary algorithms for training ensemble models based on different performance measures in software effort estimation. In Proceedings of the 9th International Conference on Predictive Models in Software Engineering - PROMISE 7813. pp. 1-10. S13*
  19. Minku, L.L. & Yao, X., 2013b. Ensembles and locality: Insight on improving software effort estimation. Information and Software Technology, 55(8), pp.1512- 1528. S11*
  20. Minku, L.L. & Yao, X., 2013c. Software Effort Estimation As a Multiobjective Learning Problem. ACM Trans.Softw.Eng.Methodol., 22(4), pp.1-32. S14*
  21. Petersen, K. et al., 2008. Systematic mapping studies in software engineering. EASE'08 Proceedings of the 12th international conference on Evaluation and Assessment in Software Engineering, pp.68-77.
  22. Seni, G. & Elder, J.F., 2010. Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions,
  23. Shepperd, M.J. & Kadoda, G., 2001. Comparing software prediction techniques using simulation. IEEE Transactions on Software Engineering, 27(11), pp.1014-1022.
  24. Song, L., Minku, L.L. & Yao, X., 2013. The impact of parameter tuning on software effort estimation using learning machines. In Proceedings of the 9th International Conference on Predictive Models in Software Engineering. S12*
  25. Vinaykumar, M.C.K, Ravi, V., 2009. Software cost estimation using soft computing approaches. In Handbook of Research on Machine Learning Applications and Trends, ed. Handbook of Research on Machine Learning Applications and Trends. IGIglobal, pp. 499-518. S16*
  26. Wen, J. et al., 2012. Systematic literature review of machine learning based software development effort estimation models. Information and Software Technology, 54(1), pp.41-59.
  27. Wu, D., Li, J. & Liang, Y., 2013. Linear combination of multiple case-based reasoning with optimized weight for software effort estimation. The Journal of Supercomputing, 64(3), pp.898-918. S4*
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Paper Citation


in Harvard Style

Idri A., Hosni M. and Abran A. (2016). Systematic Mapping Study of Ensemble Effort Estimation . In Proceedings of the 11th International Conference on Evaluation of Novel Software Approaches to Software Engineering - Volume 1: ENASE, ISBN 978-989-758-189-2, pages 132-139. DOI: 10.5220/0005822701320139


in Bibtex Style

@conference{enase16,
author={Ali Idri and Mohamed Hosni and Alain Abran},
title={Systematic Mapping Study of Ensemble Effort Estimation},
booktitle={Proceedings of the 11th International Conference on Evaluation of Novel Software Approaches to Software Engineering - Volume 1: ENASE,},
year={2016},
pages={132-139},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005822701320139},
isbn={978-989-758-189-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th International Conference on Evaluation of Novel Software Approaches to Software Engineering - Volume 1: ENASE,
TI - Systematic Mapping Study of Ensemble Effort Estimation
SN - 978-989-758-189-2
AU - Idri A.
AU - Hosni M.
AU - Abran A.
PY - 2016
SP - 132
EP - 139
DO - 10.5220/0005822701320139