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Understanding Software Estimating Models    

This two day course is designed to provide you with an advanced mathematics-oriented understanding of currently-available software estimating models and products.

Who Should Attend 

Anyone interested in the mathematical basis of software estimating models and products, the potential benefits being:

  • More effective use of currently-owned models and products
  • Better understanding of the basis of estimates being provided by bidders and subcontractors
  • Ability to make more informed buying decisions when in the market for estimating models and products

Purpose of the Workshop

  • Review the key elements of the software project management process and how they relate to the software development process
  • Review the core software project management metrics and the fundamental laws of software project dynamics
  • Consider a categorization of software estimating models by their mathematical forms
  • Understand the mathematical derivation of the models in each category
  • Understand the behavioral differences between models and how to quantify those differences
  • Understand the stochastic nature of these models and the proper random variable treatment of the independent and dependent variables

What You Will Learn

Measurement Objectifies Management

  • Software development is a process
  • Measurable process predictable outcome
  • Time, effort, defects volume AND efficiency AND defect vulnerability
  • Estimating: judgment versus calculation

Models Can Be Categorized by their Math

  • Type 0 dart board, dice, etc.
  • Type 0.5 engineering judgment
  • Type 1 single univariate power function
  • Type 2  two univariate power functions
  • Type 3 single bivariate power function
  • Type 4 bivariate 3rd-order polynomial

Models Have Unique Behavioral Characteristics

  • Entropy
  • Economy
  • Tradeoff sensitivity

Estimating is Probabilistic

  • Independent variables as random variables
  • Selecting appropriate distributions to represent the independent variables
  • Using relevant probability theorems and Monte Carlo methods for  combining and convolving the independent random variables
  • Using geometric projection to solve for the correlated  dependent random variables

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