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“ 2cee” A  2 1 st   C entury  E ffort  E stimation Methodology Tim Menzies Dan Baker [email_address] [email_address] Jairus Hihn Karen Lum [email_address] [email_address] 22nd International Forum on COCOMO and Systems/Software Cost Modeling (2007)
Our Journey ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Our Journey Continued ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Some Things We Learned Along the Way
Local Calibration   Does Not Always Improve Performance   ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Stratification  Does Not Always Improve Performance ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Cost Driver Instability ,[object Object],[object Object],[object Object],[object Object]
Some Good News ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Key Research Findings ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
2cee ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Load Historical Data Use Predefined COCOMO  Coefficients Bootstrapped Local Calibration Full Local Calibration Nearest Neighbour Local Calibration Optionally Use Manual Or Automatic Feature Selection Optionally Use Manual Stratification 2CEE Define  Project  Ranges Monte Carlo Project Instances Produce Range of COCOMO Estimates
2CEE Steps Define Model Calibration Evaluate with Cross Validation Define Project Ranges Monte Carlo Estimates
 
2cee Provides Insight into  Model Performance and Tuning  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],There are many  outliers  in our data
 
Karen will be available at the tool fair Stop in and take a look under the hood
Bibliography Current Research Publications   Selecting Best Practices for Effort Estimation,  IEEE Transactions On Software Engineering , Nov 2006. (Menzies, Chen, Hihn, Lum)  Evidence-Based Cost Estimation for Better-Quality Software,  IEEE Software , July/August 2006. (Menzies and Hihn )  Studies in Software Cost Model Behavior:Do We Really Understand Cost Model Performance?,  Proceedings of the ISPA International Conference 2006 , Seattle, WA. (Lum, Hihn, Menzies) (Best Paper Award)  Simple Software Cost Analysis: Safe or Unsafe?, Proceedings of the International Workshop on Predictor Models in Software Engineering ( PROMISE 2005 ), St Louis, MS, 14 June 2005. (Menzies, Port, Hihn , Chen)  Feature Subset Selection Improves Software Cost Estimation. ( PROMISE 2005 ), St Louis, MS, 14 June 2005. (Chen, Menzies, Port, Boehm)  Validation methods for calibrating software effort models,  ICSE 2005   Proceedings , May 2005, St Louis, MS. May 2005. (Menzies, Port, Hihn, Chen)  Specialization and Extrapolation of Software Cost Models,  Proceeding in Automation in Software Engineering Conference , Nov 2005. (Menzies, Chen, Port, Hihn)  Finding the Right Data for Software Cost Modeling,  IEEE Software , Nov/Dec 2005. (Chen, Menzies, Port, Boehm)
State of the Art Best Practice ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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21st Century Effort Estimation Methodology

  • 1. “ 2cee” A 2 1 st C entury E ffort E stimation Methodology Tim Menzies Dan Baker [email_address] [email_address] Jairus Hihn Karen Lum [email_address] [email_address] 22nd International Forum on COCOMO and Systems/Software Cost Modeling (2007)
  • 2.
  • 3.
  • 4. Some Things We Learned Along the Way
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  • 6.
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  • 10.
  • 11. Load Historical Data Use Predefined COCOMO Coefficients Bootstrapped Local Calibration Full Local Calibration Nearest Neighbour Local Calibration Optionally Use Manual Or Automatic Feature Selection Optionally Use Manual Stratification 2CEE Define Project Ranges Monte Carlo Project Instances Produce Range of COCOMO Estimates
  • 12. 2CEE Steps Define Model Calibration Evaluate with Cross Validation Define Project Ranges Monte Carlo Estimates
  • 13.  
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  • 16. Karen will be available at the tool fair Stop in and take a look under the hood
  • 17. Bibliography Current Research Publications Selecting Best Practices for Effort Estimation, IEEE Transactions On Software Engineering , Nov 2006. (Menzies, Chen, Hihn, Lum) Evidence-Based Cost Estimation for Better-Quality Software, IEEE Software , July/August 2006. (Menzies and Hihn ) Studies in Software Cost Model Behavior:Do We Really Understand Cost Model Performance?, Proceedings of the ISPA International Conference 2006 , Seattle, WA. (Lum, Hihn, Menzies) (Best Paper Award) Simple Software Cost Analysis: Safe or Unsafe?, Proceedings of the International Workshop on Predictor Models in Software Engineering ( PROMISE 2005 ), St Louis, MS, 14 June 2005. (Menzies, Port, Hihn , Chen) Feature Subset Selection Improves Software Cost Estimation. ( PROMISE 2005 ), St Louis, MS, 14 June 2005. (Chen, Menzies, Port, Boehm) Validation methods for calibrating software effort models, ICSE 2005 Proceedings , May 2005, St Louis, MS. May 2005. (Menzies, Port, Hihn, Chen) Specialization and Extrapolation of Software Cost Models, Proceeding in Automation in Software Engineering Conference , Nov 2005. (Menzies, Chen, Port, Hihn) Finding the Right Data for Software Cost Modeling, IEEE Software , Nov/Dec 2005. (Chen, Menzies, Port, Boehm)
  • 18.