Software estimation

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Slides from software estimation session at C'Ville's 2010 beCamp.

Slides from software estimation session at C'Ville's 2010 beCamp.

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Transcript

  • 1. Coping with Software Estimation
    Simeon H.K. Fitch
    Mustard Seed Software, LLC
  • 2. What and Why
    We have to do it
    No one likes it
    We’re always wrong
    Real money and time is at stake
  • 3. References
    A Review of Surveys on Software Effort Estimation
    KjetilMoløkken and MagneJørgensen
    Better sure than safe? Over-confidence in judgment based software development effort prediction intervals
    MagneJørgensen, Karl HalvorTeigen, and KjetilMoløkken
  • 4. How
    Expert based methods
    Expert consultation
    Intuition and experience
    Analogy
    Model based (Software Cost Models)
    COCOMO
    Use-Case-based estimation
    FPA-metrics or other algorithm driven methods
    Other
    Price-to-win
    Capacity related
    Top-down
    Bottom-up
  • 5. Results
    Expert estimation most frequently used method
    No evidence that the use of formal methods (on average) lead to more accurate estimate
    Cost overrun more common than schedule overrun
    Average cost overrun of 30-40%
  • 6. Results
    Accuracy (according to one study)
    If cost overrun (34%)
    Over budget: 61%
    Under budget: 10%
    If schedule overrun (22%)
    Completed after schedule: 65%
    Completed before schedule: 4%
  • 7. Results
    Prediction intervals (estimate min/max)
    In one study, students provided better prediction intervals than “experts”.
    “The software professional may feel a pressure to indicate high development skills through narrow prediction intervals”
  • 8. Blame
    Cost overruns
    Over-optimistic estimates
    Changes in design or implementation
    Schedule overruns
    Optimistic planning
    Frequent changes in specification
    Frequent requests for changes by users
    Users’ lack of understanding of their own requirements
    Other (not just bad estimation)
  • 9. What do you do?
    NASA
  • 10. What do you do?
    MSS
    Complexity measure (intuition)
    Per developer conversion factor (complexity to time)
    Confidence value [0..1]