Cited By
View all- (2017)Research patterns and trends in software effort estimationInformation and Software Technology10.1016/j.infsof.2017.06.00291:C(1-21)Online publication date: 1-Nov-2017
Column Pruning Beats Stratification in Effort Estimation
Page 7
Abstract
Local calibration combined with stratification, also known as row pruning, is a common technique used by cost estimation professionals to improve model performance. The results presented in this paper raise several serious questions concerning the benefits of row pruning for improving effort estimation indicating the need to rethink standard practice. Firstly, the mean size of improvements from row pruning appears to be relatively small compared to the size of the standard deviations in effort estimation data. Secondly, the advantages of row pruning especially for the purposes of deleting spurious outliers can be achieved using column pruning much more effectively. Hence, we advise against row pruning and advocate column pruning instead.
References
[1]
{1} B. Boehm. Software Engineering Economics. Prentice Hall, 1981.
[2]
{2} 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. Prebtice Hall, 2000.
[3]
{3} G. Boetticher. When will it be done? the 300 billion dollar question, machine learner answers. IEEE Intelligent Systems , June 2003.
[4]
{4} G. Boetticher, T. Menzies, and T. Ostrand. The PROMISE Repository of Empirical Software Engineering Data, 2007. http://promisedata.org/repository.
[5]
{5} L. Briand, T. Langley, and I. Wieczorek. A replicated assessment and comparison of common software cost modeling techniques. In Proceedings of the 22nd International Conference on Software Engineering, Limerick, Ireland, pages 377-386, 2000.
[6]
{6} Z. Chen, T. Menzies, D. Port, and B. Boehm. Finding the right data for software cost modeling. IEEE Software, Nov 2005.
[7]
{7} S. Chulani, B. Boehm, and B. Steece. Bayesian analysis of empirical software engineering cost models. IEEE Transaction on Software Engineerining, 25(4), July/August 1999.
[8]
{8} D. Ferens and D. Christensen. Calibrating software cost models to Department of Defense Database: A review of ten studies. Journal of Parametrics, 18(1):55-74, November 1998.
[9]
{9} M. Garre, M. S. J.J. Cuadrado-Gallego, M. Charro, and D. Rodriguez. Segmented parametric software estimation models: Using the em algorithm with the isbsg 8 database. In 27th International Conference on Information Technology Interfaces. ITI 2005, Dubrovnik, Croatia, 2005.
[10]
{10} M. Hall and G. Holmes. Benchmarking attribute selection techniques for discrete class data mining. IEEE Transactions On Knowledge And Data Engineering, 15(6):1437-1447, 2003.
[11]
{11} R. Jensen. An improved macrolevel software development resource estimation model. In 5th ISPA Conference, pages 88-92, April 1983.
[12]
{12} M. Jorgensen. A review of studies on expert estimation of software development effort. Journal of Systems and Software , 70(1-2):37-60, 2004.
[13]
{13} C. Kemerer. An empirical validation of software cost estimation models. Communications of the ACM, 30(5):416-429, May 1987.
[14]
{14} C. Kirsopp and M. Shepperd. Case and feature subset selection in case-based software project effort prediction. In Proc. of 22nd SGAI International Conference on Knowledge-Based Systems and Applied Artificial Intelligence, Cambridge, UK, 2002.
[15]
{15} R. Kohavi and G. H. John. Wrappers for feature subset selection. Artificial Intelligence, 97(1-2):273-324, 1997.
[16]
{16} K. Lum, J. Powell, and J. Hihn. Validation of spacecraft cost estimation models for flight and ground systems. In ISPA Conference Proceedings, Software Modeling Track, May 2002.
[17]
{17} C. Mair, G. Kadoda, M. Lefley, K. Phalp, C. S. ofieldl, M. Shepperd, and S. Webster. An investigation of machine learning based prediction systems. The Journal of Systems and Software, 53(1):23-29, 2000.
[18]
{18} T. Menzies, Z. Chen, J. Hihn, and K. Lum. Selecting best practices for effort estimation. IEEE Transactions on Software Engineering, November 2006. Available from http: //menzies.us/pdf/06coseekmo.pdf.
[19]
{19} T. Menzies, D. Port, Z. Chen, J. Hihn, and S. Stukes. Validation methods for calibrating software effort models. In Proceedings, ICSE, 2005. Available from http:// menzies.us/pdf/04coconut.pdf.
[20]
{20} A. Miller. Subset Selection in Regression (second edition). Chapman & Hall, 2002.
[21]
{21} R. Park. The central equations of the price software cost model. In 4th COCOMO Users Group Meeting, November 1988.
[22]
{22} L. Putnam and W. Myers. Measures for Excellence. Yourdon Press Computing Series, 1992.
[23]
{23} J. R. Quinlan. Learning with Continuous Classes. In 5th Australian Joint Conference on Artificial Intelligence, pages 343-348, 1992. Available from http://citeseer. nj.nec.com/quinlan92learning.html.
[24]
{24} M. Shepperd and C. Schofield. Estimating software project effort using analogies. IEEE Transactions on Software Engineering , 23(12), November 1997. Available from http: //www.utdallas.edu/~rbanker/SE_XII.pdf.
Index Terms
- Column Pruning Beats Stratification in Effort Estimation
Recommendations
Column Pruning Beats Stratification in Effort Estimation
ICSEW '07: Proceedings of the 29th International Conference on Software Engineering WorkshopsLocal calibration combined with stratification, also known as row pruning, is a common technique used by cost estimation professionals to improve model performance. The results presented in this paper raise several serious questions concerning the ...
Balanced column-wise block pruning for maximizing GPU parallelism
AAAI'23/IAAI'23/EAAI'23: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial IntelligencePruning has been an effective solution to reduce the number of computations and the memory requirement in deep learning. The pruning unit plays an important role in exploiting the GPU resources efficiently. The filter is proposed as a simple pruning unit ...
Structured Pruning with Automatic Pruning Rate Derivation for Image Processing Neural Networks
ISMSI '22: Proceedings of the 2022 6th International Conference on Intelligent Systems, Metaheuristics & Swarm IntelligenceStructured pruning has been proposed for network model compression. Because most of existing structured pruning methods assign pruning rate manually, finding appropriate pruning rate to suppress the degradation of pruned model accuracy is difficult. ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
May 2007
104 pages
ISBN:0769529542
Sponsors
Publisher
IEEE Computer Society
United States
Publication History
Published: 20 May 2007
Check for updates
Qualifiers
- Article
Conference
PROMISE '07
Sponsor:
Acceptance Rates
Overall Acceptance Rate 98 of 213 submissions, 46%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 143Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 03 Oct 2024
Other Metrics
Citations
Cited By
View all- (2017)Research patterns and trends in software effort estimationInformation and Software Technology10.1016/j.infsof.2017.06.00291:C(1-21)Online publication date: 1-Nov-2017
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in