Measurement and Models

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Models are useful for interpreting the behavior of the numerical elements of the real-world entities as well as measuring them. To help the measurement process, the model of the mapping should also be supplemented with a model of the mapping domain. A model should also specify how these entities are related to the attributes and how the characteristics relate.

Measurement is of two types −

  • Direct measurement
  • Indirect measurement

Direct Measurement

These are the measurements that can be measured without the involvement of any other entity or attribute.

The following direct measures are commonly used in software engineering.

  • Length of source code by LOC
  • Duration of testing purpose by elapsed time
  • Number of defects discovered during the testing process by counting defects
  • The time a programmer spends on a program

Indirect Measurement

These are measurements that can be measured in terms of any other entity or attribute.

The following indirect measures are commonly used in software engineering.

$$\small Programmer\:Productivity = \frac{LOC \: produced }{Person \:months \:of \:effort}$$

$\small Module\:Defect\:Density = \frac{Number \:of\:defects}{Module \:size}$

$$\small Defect\:Detection\:Efficiency = \frac{Number \:of\:defects\:detected}{Total \:number \:of\:defects}$$

$\small Requirement\:Stability = \frac{Number \:of\:initial\:requirements}{Total \:number \:of\:requirements}$

$\small Test\:Effectiveness\:Ratio = \frac{Number \:of\:items\:covered}{Total \:number \:of \:items}$

$\small System\:spoilage = \frac{Effort \:spent\:for\:fixing\:faults}{Total \:project \:effort}$

Measurement for Prediction

For allocating the appropriate resources to the project, we need to predict the effort, time, and cost for developing the project. The measurement for prediction always requires a mathematical model that relates the attributes to be predicted to some other attribute that we can measure now. Hence, a prediction system consists of a mathematical model together with a set of prediction procedures for determining the unknown parameters and interpreting the results.

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