What is Function Point Analysis?
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The function point $(F P)$ metric is used effectively for measuring the size of a software system. Function based metrics can be used as a predictor for the overall testing effort. Various project-level characteristics (e.g., and testing effort and time, errors uncovered, and number of test cases produced) of past projects can be collected and correlated with the number of $F P$ produced by a project team. The team can then project the expected values of these characteristics for the current project.

Listed below are a few $F P$ measures:

  1. Number of hours required for testing per $F P$
  2. Number of $F P$ s tested per person-month
  3. Total cost of testing per $F P$
  4. Defect density measures the number of defects identified across one or more phases of the development project lifecycle and compares that value with the total size of the system. It can be used to compare the density levels across different lifecycle phases or across different development efforts. It is calculated as

    $$\text{Number of defects (by phase or in total) / Total number of FPs}$$

  5. Test case coverage measures the number of test cases that are necessary to adequately support thorough testing of a development project. This measure does not indicate the effectiveness of test cases, nor does it guarantee that all conditions have been tested. However, it can be an effective comparative measure to forecast anticipated requirements for testing that may be required on a development system of a particular size. This measure is calculated as

    $$\text{Test case coverage = Number of test cases / Total number of F Ps}$$

Capers Jones estimates that the number of test cases in a system can be determined by the function points estimate for the corresponding effort. The formula is

$$\text{Number of test cases = (Function points)}^{1.2}$$

Function points can also be used to measure the acceptance test cases. The formula is

$$ \text { Number of test cases = (Function points) } \times 1.2 $$

The afore-mentioned relationships show that test case grow at a faster rate than function points.

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