Local bias and its impacts on the performance of parametric estimation models

ACM International Conference Proceeding Series

Volumn , Issue , 2011, Pages

  Yang, Ye ^a   Xie, Lang ^a,b   He, Zhimin ^a,b   Li, Qi ^c   Nguyen, Vu ^c   Boehm, Barry ^c   Valerdi, Ricardo ^d  

^a INSTITUTE OF SOFTWARE   (China)

^b UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

^c University of Southern California   (United States)

^d MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Accuracy indicator; Effort estimation; Local bias; Parametric model

Indexed keywords

ACCURACY INDICATORS; COCOMO II; CORRELATION ANALYSIS; DATA SETS; DATA SUBSETS; EFFORT ESTIMATION; ESTIMATION PERFORMANCE; HISTORICAL DATA; LOCAL BIAS; PARAMETRIC ESTIMATION; PARAMETRIC MODELS; POSITIVE CORRELATIONS; USAGE PATTERNS;

ESTIMATION; MODELS; PREDICTIVE CONTROL SYSTEMS; SOFTWARE ENGINEERING;

PARAMETER ESTIMATION;

EID: 80054069256     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2020390.2020404     Document Type: Conference Paper

References (27)

1. Boehm, B. W., Clark, and Horowitz et al. Software Cost Estimation with Cocomo II with Cdrom: Prentice Hall PTR, 2000.
2. SEER-SEM Version 51 and Later User's Manual, Galorath Incorporated, March 2000 update.
3. Your Guide to PRICE S: Estimating Cost and Schedule of Software Development and Support, Mt. Laurel, New Jersey, PRICE Systems, LLC: 1998.
4. NASA Cost Estimation handbook, http://www.ceh.nasa.gov/webhelpfiles/Cost- Estimating-Handbook-NASA-2004.htm
5. Handbook for Software Cost Estimation (JPL). http://www.ceh.nasa.gov/ downloadfiles/Web%20Links/cost-hb-public-6-5.pdf
6. Stutzke, R. D. Estimating Software-Intensive Systems: Projects, Products, and Processes. Addison-Wesley Professional (May 6, 2005).
7. Chen, J., Yang, Y., and Nguyen, V. et al. Reducing the Local Bias in Calibrating the General COCOMO. 24th International Forum on COCOMO and Systems/Software Cost Modeling. Nov. 2-5, 2009. Cambridge, USA.
8. Bottou, L., and Bousquet, O., The Tradeoffs of Large Scale Learning. Advances in Neural Information Processing Systems, 20:161-168, 2008
9. Hastie, T., Tibshirani, R., and Friedman, J., The elements of statistical learning data mining, inference, and prediction. Springer; Corrected edition. July 30, 2003.
10. Wasserman, L. All of Statistics: A Concise Course in Statistical Inference. Springer-Verlag, Berlin.2003.
11. Kitchenham, B. A., Mendes, E., and Travassos, G. H. "Cross versus within-company cost estimation studies: A systematic review," IEEE Transactions on Software Engineering, vol. 33, no. 5, pp. 316-329, May, 2007.
12. Jeffery, R., Ruhe, M., and Wieczorek, I. "A comparative study of two software development cost modeling techniques using multi-organizational and company-specific data," Information and Software Technology, vol. 42, no. 14, pp. 1009-1016, Nov, 2000.
13. Kitchenham, B. "A procedure for analyzing unbalanced datasets," IEEE Transactions on Software Engineering, vol. 24, no. 4, pp. 278-301, Apr, 1998.
14. Liu, Q., and Mintram, R. "Preliminary data analysis methods in software estimation," Software Quality Journal, vol. 13, no. 1, pp. 91-115, Mar, 2005.
15. Cuadrado-Gallego, J. J., and Sicilia, M. A. "An algorithm for the generation of segmented parametric software estimation models and its empirical evaluation," Computing and Informatics, vol. 26, no. 1, pp. 1-15, 2007.
16. Clark, B., Devnani-Chulani, S. and Boehm B. W. et al., "Calibrating the COCOMO II Post-Architecture model," Proceedings of the 1998 International Conference on Software Engineering, International Conference on Software Engineering, pp. 477-480, Los Alamitos: IEEE Computer Soc, 1998.
17. Nguyen, V., Steece, B., and Boehm, B. W. et al., A Constrained Regression Technique for COCOMO Calibration, New York: Assoc Computing Machinery, 2008.
18. Chulani, S., Boehm, B. W., and Steece, B. "Bayesian analysis of empirical software engineering cost models," IEEE Transactions on Software Engineering, vol. 25, no. 4, pp. 573-583, Jul-Aug, 1999.
19. Yang, Y., Clark, B. COCOMO II.2004 Calibration Status. 19th International Forum on COCOMO and Systems/Software Cost Modeling. Oct. 22-25, 2004. Los Angeles, USA.
20. Menzies, T., Hihn, J. Evidence-Based Cost Estimation for Better Quality Software. IEEE Software. July/August 2006 .
21. Menzies, T., Chen, Z., and Hihnet, J. et al. Selecting Best Practices for Effort Estimation. IEEE Transactions on Software Engineering. November 2006.
22. Xie, L., Yang, Y., and Yang, D. et al. Mean-Variance Combination (MVC): A New Method for Evaluating Effort Estimation Models. Accepted by the Symposium in Honor of Dr. Barry Boehm. April 25-26, 2011. Beijing China.
23. Kocaguneli, E., Menzies, T., and Bener, A. et al. "Exploiting the Essential Assumptions of Analogy-Based Effort Estimation,"IEEE Transactions on Software Engineering, 02 Mar. 2011. IEEE computer Society Digital Library. IEEE Computer Society
24. Port, D., and Korte, M. (2008). Comparative Studies of the Model Evaluation Criterions MMRE and PRED on Software Cost Estimation Research. ACM, Liepzig, Germany, pp. 63-70.
25. Jørgensen, M., Evidence-based guidelines for assessment of software development cost uncertainty. Software Engineering, IEEE Transactions on, 2005. 31(11): p. 942-954.
26. Jørgensen, M., and Teigen, K. H. 2002. Uncertainty intervals versus interval uncertainty: An alternative method for eliciting effort prediction intervals in software development projects. International Conference on Project Management (ProMAC), Singapore, pp. 343-352.
27. Jørgensen, M., Teigen, K. H., and Moløkken-østvold, K. J, 2004. Better sure than safe? Overconfidence in judgment based software development effort prediction intervals. Journal of Systems and Software 70(1-2): 79-93.