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An Effort Estimation Model for Software Development using Ensemble Learning

by Abhishek Kumar, Unmukh Datta
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 115 - Number 21
Year of Publication: 2015
Authors: Abhishek Kumar, Unmukh Datta
10.5120/20279-2713

Abhishek Kumar, Unmukh Datta . An Effort Estimation Model for Software Development using Ensemble Learning. International Journal of Computer Applications. 115, 21 ( April 2015), 37-41. DOI=10.5120/20279-2713

@article{ 10.5120/20279-2713,
author = { Abhishek Kumar, Unmukh Datta },
title = { An Effort Estimation Model for Software Development using Ensemble Learning },
journal = { International Journal of Computer Applications },
issue_date = { April 2015 },
volume = { 115 },
number = { 21 },
month = { April },
year = { 2015 },
issn = { 0975-8887 },
pages = { 37-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume115/number21/20279-2713/ },
doi = { 10.5120/20279-2713 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:55:31.748106+05:30
%A Abhishek Kumar
%A Unmukh Datta
%T An Effort Estimation Model for Software Development using Ensemble Learning
%J International Journal of Computer Applications
%@ 0975-8887
%V 115
%N 21
%P 37-41
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

For a successful project development, it is important for any software organization that the project should be completed within time and budget, and the project should have requisite quality. This paper presents an Ensemble learning based Adaptive Neuro-Fuzzy Approach for Software Development Time Estimation. The concept behind this technique is based on ensemble learning methods. This technique combines multiple models into one model. The ensemble fits a new learner to the difference between the experiential response and the aggregated prediction of all learners which grown previously. In this paper, we describe a brief literature review of different techniques of software development time estimation along with a comparison of different techniques with our approach.

References
  1. M. Chemuturi, "Software Estimation Best Practices, Tools & Techniques: A Complete Guide for Software Projects Estimator", available at :http://books. google. co. in/books?id=IwEOB2Mfzx0C&pg=PA1 &source=gbs _toc_r &cad=4#v=onepage&q&f=false, 2009.
  2. S Basha and P. Dhavachelvan, "Analysis of Empirical Software Effort EstimationModels", International Journal of Computer Science and Information Security (IJCSIS), Vol. 7, No. 3, 2010.
  3. B. Hughes and M. Cotterell, "Software Project Management", Tata McGraw-Hill,2006.
  4. B. W. Boehm, "Software Engineering Economics", Prentice-Hall, Englewood Cliffs, NJ, USA, 1981.
  5. T. Gruschke, "Empirical Studies of Software Cost Estimation: Training of Effort Estimation Uncertainty Assessment Skills", 11th IEEE International Software Metrics Symposium, IEEE, 2005.
  6. C. C. Kung and J. Y. Su, "Affine Takagi-Sugeno fuzzy modeling algorithm by Fuzzy c-regression models clustering with a novel cluster validity criterion", IETControl Theory Appl. , pp. 1255 – 1265, 2007.
  7. V. Khatibi, Dayang and N. A. Jawawi, " Software Cost Estimation Methods: AReview", Journal of Emerging Trends in Computing and Information Sciences, CIS Journal, Vol. 2, no. 1, ISSN 2079-8407, 2011.
  8. N. Sharma1, A. Bajpai and M. R. Litoriya, "A Comparison of Software Cost Estimation Methods: A Survey", The International Journal of Computer Science and Applications (TIJCSA), Vol. 1, no. 3, ISSN – 2278 – 1080, May 2012.
  9. J. Keung, "Software Development Cost Estimation Using Analogy: A Review", Australian Software Engineering conference, IEEE, 2009, DOI:10. 1109/ASWEC. 2009. 32, 1530-0803/09.
  10. T. R. Benala, S. Dehuri and R. Mall, "Computational Intelligence in Software Cost Estimation: An Emerging Paradigm", ACM SIGSOFT Software Engineering NotesPage, Vol. 37, no. 3, 2012, DOI: 10. 1145/180921. 2180932.
  11. J. S. Pahariya, V. Ravi and M. Carr, "Software Cost Estimation using Computational Intelligence Techniques", World Congress on Nature & Biologically Inspired Computing(NaBIC 2009)978-1-4244-5612-3/09/2009 IEEE, 2009.
  12. Mrinal Kanti Ghose, Roheet Bhatnagar and Vandana Bhattacharjee. "Comparing Some Neural Network Models forSoftware Development Effort Prediction", IEEE 2011
  13. Venus Marza, Amin Seyyedi, and Luiz Fernando Capretz, "Estimating Development Time of Software Projects Using a Neuro Fuzzy Approach", World Academy of Science, Engineering and Technology 22- 2008.
  14. Vachik S. Dave Kamlesh Dutta, Neural Network based Software Effort Estimation & Evaluation criterion MMRE, International Conference on Computer & Communication Technology (ICCCT)-2011.
  15. Cuauhtémoc López Martín, Software Development Effort Estimation Using Fuzzy Logic: A Case Study, Proceedings of the Sixth Mexican International Conference on Computer Science (ENC'05), 0-7695-2454-0/05 $20. 00 © IEEE 2005.
  16. Moataz A. Ahmed, Moshood Omolade Saliuand Jarallah Al Ghamdi, "Adaptive fuzzy logic-based framework for software development effort prediction", Information and Software Technology 47- 2005.
  17. C. J. Burgess, M. Lefley, Can genetic programming improve software effort estimation? A comparative evaluation, Information and Software Technology 43 (2001) 863–873.
  18. Anish Mittal,Kamal Parkash,Harish Mittal " Software Cost Estimation Using Fuzzy Logic", ACM SIGSOFT Software Engineering Notes Page 1 November 2010 Volume 35 Number 1.
Index Terms

Computer Science
Information Sciences

Keywords

Membership Function (MF) COCOMO Adaptive Neuro Fuzzy Inference System (ANFIS) Neural Network Fuzzy Logic Prediction MRE MMRE BRE Development Time (DT)

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