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Automated Software Cost Estimation

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					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
   Goal of software engineering is to deliver
    software on time and within budget
Possible Solution

   Automated Software Cost Estimation
    –   Look at history
    –   Generalize data
    –   Create equations
    –   Parametric
Input Measurements

   SLOC – Source Lines of Code
   DSI – Delivered Source Instructions
   Function Points
Cost Estimation Models

   COCOMO 81
   COCOMO II
   REVIC
   SLIM
   Others
COCOMO

   Developed by Barry Boehm in 81
   Based on historical database
   DSI is the input
   Three versions
    –   Basic Model
    –   Intermediate Model
    –   Detailed Model
COCOMO II

   Updated the COCOMO 81 model
   Allows for
    –   Spiral development
    –   Rapid prototyping
    –   COTS integration
    –   OO Design
   Uses SLOC
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
SLIM

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