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Automated Software Cost Estimation

Automated Software Cost Estimation. By James Roberts EEL 6883 Spring 2007. Background. 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

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Automated Software Cost Estimation

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  1. Automated Software Cost Estimation By James Roberts EEL 6883 Spring 2007

  2. Background • 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

  3. Possible Solution • Automated Software Cost Estimation • Look at history • Generalize data • Create equations • Parametric

  4. Input Measurements • SLOC – Source Lines of Code • DSI – Delivered Source Instructions • Function Points

  5. Cost Estimation Models • COCOMO 81 • COCOMO II • REVIC • SLIM • Others

  6. COCOMO • Developed by Barry Boehm in 81 • Based on historical database • DSI is the input • Three versions • Basic Model • Intermediate Model • Detailed Model

  7. COCOMO II • Updated the COCOMO 81 model • Allows for • Spiral development • Rapid prototyping • COTS integration • OO Design • Uses SLOC

  8. REVIC • 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

  9. SLIM • Software Life-Cycle Model • Developed by Larry Putnam • Uses a Rayleigh distribution • Project personnel vs. Time • Intended for large projects • Fewer parameters

  10. QSM’s SLIM Tool • Based on the SLIM model • Windows based • Easy to use • Several different wizards for quickly generating an estimate • Five steps to create an estimate

  11. Softstar’s CoStar • 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

  12. Galorath’s SEER-SEM • 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

  13. Conclusion • Would recommend the Softstar CoStar software • Software Cost Estimation is important for any program manager • These tools are vital to quickly generating estimates for success

  14. References • 1. Dave Srulowitz, M.B., Vic Helbling. Software Estimation. 2001 [cited; Available from: http://www.saspin.org/SASPIN_Apr2001_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. 1st 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.

  15. References Cont. • 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. 177-205. • 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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