Software effort estimation based on optimized model tree

ACM International Conference Proceeding Series

Volumn , Issue , 2011, Pages

  Azzeh, Mohammad ^a  

^a APPLIED SCIENCE PRIVATE UNIVERSITY   (Jordan)

Author keywords

Bees algorithm; Model tree; Software effort estimation

Indexed keywords

BEES ALGORITHMS; CROSS VALIDATION; DATA SETS; EFFORT ESTIMATION; EFFORT ESTIMATION MODEL; ESTIMATION METHODS; ESTIMATION MODELS; LARGE SPACES; MODEL TREE; MODEL TREES; MULTI LAYER PERCEPTRON; OPTIMAL CHOICE; OPTIMAL PARAMETER; OPTIMIZATION ALGORITHMS; PREDICTION ACCURACY; REGRESSION PROBLEM; REGRESSION TECHNIQUES; SOFTWARE EFFORT ESTIMATION; STEPWISE REGRESSION;

CASE BASED REASONING; ESTIMATION; KNOWLEDGE BASED SYSTEMS; LEARNING ALGORITHMS; LEARNING SYSTEMS; OPTIMIZATION; PLANT EXTRACTS; PREDICTIVE CONTROL SYSTEMS; REGRESSION ANALYSIS; SOFTWARE ENGINEERING;

PARAMETER ESTIMATION;

EID: 80054066579     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2020390.2020396     Document Type: Conference Paper

References (26)

1. Adriano L. O., Petrônio L. B., Ricardo M. F., Márcio C. (2010) GA-based method for feature selection and parameters optimization for machine learning regression applied to software effort estimation. Information & Software Technology 52(11): 1155-1166.
2. Auer M, Trendowicz A, Graser B, Haunschmid E, Biffl S (2006) Optimal project feature weights in analogybased cost estimation: Improvement and limitations. IEEE Trans Softw Eng 32:83-92. doi:10.1109/ TSE.2006.1599418
3. Azzeh, M., Neagu, D., Cowling, P., (2010) Fuzzy grey relational analysis for software effort estimation, Empir softw Eng, 60-90 DOI. 10.1007/s10664-009- 9113-0.
4. Boetticher, G., Menzies, T., Ostrand, T. PROMISE Repository of empirical software engineering data http://promisedata.org/repository, West Virginia University, Department of Computer Science.
5. Briand LC, El-Emam K, Surmann D, Wieczorek I, Maxwell KD (999) An assessment and comparison of common cost estimation modeling techniques. Proceeding of the 1999 International Conference on Software Engineering, 313-322
6. Chiu NH, Huang SJ (2007) The adjusted analogy-based software effort estimation based on similarity distances. J Syst Softw 80:628-640. doi:10.1016/j.jss.2006.06.006
7. Idri, A., Abran, A., Khoshgoftaar, T., 2001. Fuzzy Analogy: a New Approach for Software Effort Estimation, In: 11th International Workshop in Software Measurements, pp. 93-101.
8. Jorgensen M, Indahl U, Sjoberg D (2003) Software effort estimation by analogy and "regression toward the mean". J Syst Softw 68:253-262. doi:10.1016/S0164-1212(03)00066-9
9. Kramer, S., G. Widmer, B. Pfahringer, and M. DeGroeve. (2001) Prediction of ordinal classes using regression trees. Fundamenta Informaticae, 47:1-13.
10. E. Kocaguneli E., Menzies, T., Bener A. and Keung, J. W. TEAK (2007): Learning Better Case Selection Strategies for Analogy Based Software Cost Estimation, IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, VOL. 6, NO. 1, 2007.
11. Li JZ, Ruhe G, Al-Emran A, Richter M (2007) A flexible method for software effort estimation by analogy. Empir Softw Eng 12(1):65-106. doi:10.1007/s10664-006-7552-4
12. Li YF, Xie M, Goh TN (2009), A study of A study of the nonlinear adjustment for analogy based software cost estimation. Empir Softw Eng 14:603-643
13. Mendes E, Mosley N, Counsell S (2003) A replicated assessment of the use of adaptation rules to improve Web cost estimation, International Symposium on Empirical Software Engineering, pp. 100-109.
14. Mendes E, Watson I, Triggs C, Mosley N, Counsell S (2003) A comparative study of cost estimation models for web hypermedia applications. Empir Softw Eng 8:163-196. doi:10.1023/A:1023062629183
15. Menzies, T., Chen Z., Hihn, J. Lum, K. (2006). Selecting Best Practices for Effort Estimation. IEEE Transaction on Software Engineering. 32, 883-895.
16. Menzies T., Hihn, J. "Evidence-based cost estimation for better-quality software," IEEE Softw., vol. 23, no. 4, pp. 64-66, 2006.
17. Petrônio L. B., Adriano L. O., Gustavo H. T. R., Silvio R. L. M. (2007) Bagging Predictors for Estimation of Software Project Effort. IJCNN 2007: 1595-1600.
18. Pham, D.T., Ghanbarzadeh, A., Koç, E., Otri , S., Rahim , S. and Zaidi, M. The Bees Algorithm - A novel tool for complex optimisation problems (2006), Proceedings of the 2nd Virtual International Conference on Intelligent Production Machines and Systems (I*PROMS-06), Cardiff, UK, 454-459
19. Pedrycz, W. (2002). Computational Intelligence As an Emerging Paradigm of Software Engineering. In A CM International Conference Proceeding Series New York, NY, USA pp. 7-14.
20. Shepperd M, Schofield C (1997) Estimating software project effort using analogies. IEEE Trans Softw Eng 23:736-743. doi:10.1109/32.637387
21. Shepperd M, Kadoda G (2001) Comparing software prediction techniques using simulation. IEEE Trans Softw Eng 27(11):1014-1022. doi:10.1109/32.965341
22. Shepperd M, Cartwright M, "A Replication of the Use of Regression towards the Mean (R2M) as an Adjustment to Effort Estimation Models," metrics, pp.38, 11th IEEE International Software Metrics Symposium (METRICS'05), 2005
23. Ross J. Quinlan: Learning with Continuous Classes (1992) In: 5th Australian Joint Conference on Artificial Intelligence, Singapore, 343-348.
24. Walkerden F, Jeffery DR (1999) An empirical study of analogy-based software effort Estimation. Empir Softw Eng 4(2):135-158. doi:10.1023/A: 1009872202035.
25. Wang ,Y., Witten, I. H. (1997) Induction of model trees for predicting continuous classes. In: Poster papers of the 9th European Conference on Machine Learning.
26. ISBSG, (2007), International Software Benchmarking standards Group, Data repository release 10, Site: http://www.isbsg.org, (last access, 2009).