Automated Software Cost Estimation By James Roberts EEL
Automated Software Cost Estimation By James Roberts EEL 6883 Spring 2007
Background l l Over 53% of software projects overrun by more than 50% in both budget and schedule Software overrun in budget is a failure Software overrun in schedule is a failure Goal of software engineering is to deliver software on time and within budget
Possible Solution l Automated Software Cost Estimation – – Look at history Generalize data Create equations Parametric
Input Measurements l l l SLOC – Source Lines of Code DSI – Delivered Source Instructions Function Points
Cost Estimation Models l l l COCOMO 81 COCOMO II REVIC SLIM Others
COCOMO l l Developed by Barry Boehm in 81 Based on historical database DSI is the input Three versions – – – Basic Model Intermediate Model Detailed Model
COCOMO II l l Updated the COCOMO 81 model Allows for – – l Spiral development Rapid prototyping COTS integration OO Design Uses SLOC
REVIC l l l Revised Intermediate COCOMO Developed by Ray Kile Updated to use Air Force project data Adds a mode for Ada development Inputs are the same as COCOMO 81
SLIM l l l Software Life-Cycle Model Developed by Larry Putnam Uses a Rayleigh distribution – l l Project personnel vs. Time Intended for large projects Fewer parameters
QSM’s SLIM Tool l l Based on the SLIM model Windows based Easy to use Several different wizards for quickly generating an estimate Five steps to create an estimate
Softstar’s Co. Star l l l l Based on the COCOMO model Windows based Easy to use Many different COCOMO variations Create Estimate Wizard Many parameters required Highly configurable Full featured demo version available
Galorath’s SEER-SEM l l l l Based on proprietary COCOMO-like models Windows based Moderately easy to use Create Estimate Wizard Few parameters required up front Highly configurable Poor demo version
Conclusion l l l Would recommend the Softstar Co. Star software Software Cost Estimation is important for any program manager These tools are vital to quickly generating estimates for success
References l l l 1. Dave Srulowitz, M. B. , Vic Helbling. Software Estimation. 2001 [cited; Available from: http: //www. saspin. org/SASPIN_Apr 2001_COCOMO. pdf. 2. Briand, L. C. , et al. An assessment and comparison of common software cost estimation modeling techniques. 1999. 3. Boehm, B. W. , Software Engineering Economics. 1 st ed. 1981: Prentice-Hall. 4. COCOMO II. [cited; Available from: http: //en. wikipedia. org/wiki/COCOMO_II. 5. Boehm, B. C. , B. ; Horowitz, E. ; Madachy, R. ; Shelby, R. ; Westland, C. An Overview of the COCOMO 2. 0 Software Cost Model. in Software Technology Conference. 1995. 6. Systems, S. Overview of COCOMO. 2007 [cited; Available from: http: //www. softstarsystems. com/overview. htm.
References Cont. l l l l 7. C. Abts, B. C. , S. Devnani-Chulani, E. Horowitz, R. Madachy, D. Reifer, R. Selby, B. Steece, COCOMO II Model Definition Manual. Technical report, Center for Software Engineering, USC. 1998. 8. Albrecht, A. , Function Points: A New Way of Looking at Tools. 1979. 9. Parametric Cost Estimating Handbook. US Dept. of Defense, Washington D. C. , 1995. 10. Agency, D. C. M. DCMA Guidebook - Software Acquisition Management. 2007 [cited. 11. Boehm, B. A. , C. ; Chulani, S. , Software Development Cost Estimation Approaches - A Survey. Annals of Software Engineering, 2000. 10(1 -4): p. 177205. 12. Chris, F. K. , An empirical validation of software cost estimation models. Commun. ACM, 1987. 30(5): p. 416 -429. 13. Sultanodlu, S. Software Measurement, Cost Estimation, SLIM, COCOMO. 1998 [cited; Available from: http: //yunus. hacettepe. edu. tr/~sencer/cocomo. html
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