Case-Based Reasoning for Reducing Software Development Effort

Adam Brady, Tim Menzies, Oussama El-Rawas, Ekrem Kocaguneli, Jacky W. Keung

.

DOI: 10.4236/jsea.2010.311118                

Abstract

How can we best find project changes that most improve project estimates? Prior solutions to this problem required the use of standard software process models that may not be relevant to some new project. Also, those prior solutions suffered from limited verification (the only way to assess the results of those studies was to run the recommendations back through the standard process models). Combining case-based reasoning and contrast set learning, the W system requires no underlying model. Hence, it is widely applicable (since there is no need for data to conform to some software process models). Also, W’s results can be verified (using holdout sets). For example, in the experiments reported here, W found changes to projects that greatly reduced estimate median and variance by up to 95% and 83% (respectively).

Keywords

Software Effort Estimation, Case Based Reasoning, Effort Estimation

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	B. Boehm, E. Horowitz, R. Madachy, D. Reifer, B. K. Clark, B. Steece, A. W. Brown, S. Chulani and C. Abts, “Software Cost Estimation with Cocomo II,” Prentice Hall, New Jersey, 2000.
[2]	M. Shepperd and C. Schofield, “Estimating Software Project Effort Using Analogies,” IEEE Transactions on Software Engineering, Vol. 23, No. 11, November 1997, pp. 736-743.
[3]	Y. Li, M. Xie and T. Goh, “A Study of Project Selection and Feature Weighting for Analogy Based Software Cost Estimation,” Journal of Systems and Software, Vol. 82, No. 2, February 2009, pp. 241-252.
[4]	O. El-Rawas, “Software Process Control without Calibration,” Master’s Thesis, Morgantown, 2008.
[5]	T. Menzies, O. El-Rawas, J. Hihn and B. Boehm, “Can We Build Software Faster and Better and Cheaper?” Proceedings of the 5th International Conference on Predictor Models in Software Engineering (PROMISE’09), 2009.
[6]	T. Menzies, O. Elrawas, J. Hihn, M. Feathear, B. Boehm and R. Madachy, “The Business Case for Automated Software Engineering,” Proceedings of the Twenty-second IEEE/ACM International Conference on Automated Software Engineering (ASE’07), 2007, pp. 303-312.
[7]	T. Menzies, S. Williams, O. El-rawas, B. Boehm and J. Hihn, “How to Avoid Drastic Software Process Change (Using Stochastic Statbility),” Proceedings of the 31st International Conference on Software Engineering (ICSE’09), 2009.
[8]	K. Cowing, “Nasa to Shut down Checkout & Launch Control System,” August 26, 2002. http://www.spaceref.com /news/viewnews.html
[9]	B. Boehm, “Software Engineering Economics,” Prentice Hall, New Jersey, 1981.
[10]	C. Kemerer, “An Empirical Validation of Software Cost Estimation Models,” Communications of the ACM, Vol. 30, No. 5, May 1987, pp. 416-429.
[11]	D. Baker, “A Hybrid Approach to Expert and Model-Based Effort Estimation,” Master’s Thesis, Morgantown, 2007.
[12]	P. Green, T. Menzies, S. Williams and O. El-waras, “Understanding the Value of Software Engineering Technologies,” Proceedings of the 2009 IEEE/ACM International Conference on Automated Software Engineering (ASE’09), 2009.
[13]	T. Menzies, O. Elrawas, D. Baker, J. Hihn and K. Lum, “On the Value of Stochastic Abduction (If You Fix Everything, You Lose Fixes for Everything Else),” International Workshop on Living with Uncertainty (An ASE’07 Co-Located Event), 2007.
[14]	R. C. Schank, “Dynamic Memory: A Theory of Reminding and Learning in Computers and People,” Cambridge University Press, New York, 1983.
[15]	A. Aamodt and E. Plaza, “Case-Based Reasoning: Foundational Issues, Methodological Variations and System Approaches,” Artificial Intelligence Communications, Vol. 7, No. 1, 1994, pp. 39-59.
[16]	M. J. Shepperd, “Case-Based Reasoning and Software Engineering,” Technical Report TR02-08, Bournemouth University, UK, 2002.
[17]	G. Kadoda, M. Cartwright, L. Chen and M. Shepperd, “Experiences Using Casebased Reasoning To Predict Software Project Effort,” Keele University, Staffordshire, 2000.
[18]	E. Mendes, I. D. Watson, C. Triggs, N. Mosley and S. Counsell, “A Comparative Study of Cost Estimation Models for Web Hypermedia Applications,” Empirical Software Engineering, Vol. 8, No. 2, June 2003, pp. 163-196.
[19]	T. Menzies, D. Owen and J. Richardson, “The Strangest Thing about Software,” IEEE Computer, Vol. 40, No. 1, January 2007, pp. 54-60.
[20]	T. Menzies and H. Singh, “Many Maybes Mean (Mostly) the Same Thing,” In: M. Madravio, Ed., Soft Computing in Software Engineering, Springer-Verlag, Berlin Heidelberg, 2003.
[21]	M. Mo?ina, J. Demsar, M. Kattan and B. Zupan, “Nomograms for Visualization of Naive Bayesian Classifier,” Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD’04), Vol. 3202, 2004, pp. 337-348.
[22]	F. P. Brooks, “The Mythical Man-Month,” Anniversary Edition, Addison-Wesley, Massachusetts, 1975.
[23]	H. Zhang, M. Huo, B. Kitchenham and R. Jeffery, “Qualitative Simulation Model for Software Engineering Process,” Proceedings of the Australian Software Engineering Conference (ASWEC'06), 2006, pp. 10-400.
[24]	J. Dougherty, R. Kohavi and M. Sahami, “Supervised and Unsupervised Discretization of Continuous Features,” International Conference on Machine Learning, 1995, pp. 194-202.
[25]	Y. Yang and G. I. Webb, “A Comparative Study of Discretization Methods for Naive-Bayes Classifiers,” Proceedings of PKAW 2002: The 2002 Pacific Rim Knowledge Acquisition Workshop, 2002, pp 159-173.
[26]	D. Milicic and C. Wohlin, “Distribution Patterns of Effort Estimations,” 30th EUROMICRO Conference (EUROMICRO'04), 2004.
[27]	B. Kitchenham, E. Mendes and G. H. Travassos, “Cross Versus Within-Company Cost Estimation Studies: A Systematic Review,” IEEE Transactions on Software Engineering, Vol. 33, No. 5, May 2007, pp. 316-329.
[28]	E. Alpaydin, “Introduction to Machine Learning,” MIT Press, Cambridge, 2004.