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JULIO GONZALEZ SANZ
JULIO GONZALEZ SANZ
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1© 2008 Carnegie Mellon University Healthy Ingredients of CMMI Process Performance Models 1. Statistical, probabilistic or simulation in nature 2. Predict interim and/or final project outcomes 3. Use controllable factors tied to sub-processes to conduct the prediction 4. Model the variation of factors and understand the predicted range or variation of the outcomes 5. Enable “what-if” analysis for project planning, dynamic re-planning and problem resolution during project execution 6. Connect “upstream” activity with “downstream” activity 7. Enable projects to achieve mid-course corrections to ensure project success
© 2008 Carnegie Mellon University QQual All Models (Qualitative and Quantitative) Quantitative Models (Deterministic, Statistical, Probabilistic) Statistical or Probabilistic Models Interim outcomes predicted Controllable x factors involved Process Performance Model - With controllable x factors tied to Processes and/or Sub- processes Anecdotal Biased samples No uncertainty or variation modeledOnly final outcomes are modeledOnly uncontrollable factors are modeledOnly phases or lifecycles are modeled

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Workshop healthy ingredients ppm[1]

  • 1. 1© 2008 Carnegie Mellon University Healthy Ingredients of CMMI Process Performance Models 1. Statistical, probabilistic or simulation in nature 2. Predict interim and/or final project outcomes 3. Use controllable factors tied to sub-processes to conduct the prediction 4. Model the variation of factors and understand the predicted range or variation of the outcomes 5. Enable “what-if” analysis for project planning, dynamic re-planning and problem resolution during project execution 6. Connect “upstream” activity with “downstream” activity 7. Enable projects to achieve mid-course corrections to ensure project success
  • 2. © 2008 Carnegie Mellon University QQual All Models (Qualitative and Quantitative) Quantitative Models (Deterministic, Statistical, Probabilistic) Statistical or Probabilistic Models Interim outcomes predicted Controllable x factors involved Process Performance Model - With controllable x factors tied to Processes and/or Sub- processes Anecdotal Biased samples No uncertainty or variation modeledOnly final outcomes are modeledOnly uncontrollable factors are modeledOnly phases or lifecycles are modeled