Brief about Function Point Metric for software testing

Mumbai University > Information Technology > Sem 8 > Software Testing and Quality Assurance

Marks: 5 Mar

Difficulty : Medium

mumbai university • 967  views

The function point (FP) 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 of a past projects can be collected and correlated with the number of FP produced by a project team. The team can then project the expected values of these characteristics for the current project. Listed below are few FP measures:

  1. Number of hours required for testing per FP.

  2. Number of FPs tested per person-month.

  3. Total cost of testing per FP.

  4. Defect density measures the number of defects identified across one or more phases of the development project life cycle and compares that value with the total size of the system. It can be used to compare the density levels across different life cycle phases or across different development efforts. It is calculated as –

Number of defects (by phase or in total) / Total number of FPs

  1. Test case coverage measures the number of test cases that are necessary to adequately support through 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 measurement is calculated as –

Test case coverage = $\frac{\text{number of test cases}}{ \text{total number of FPs}}$

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

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

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

Number of test cases = (function points) $\times$ 1.2

The above mentioned relationships show that test cases grow at a faster rate than function points. This is initiative because, as an application grows, the number of inter-relationships within the applications becomes more complex. For example, if a development application has 1000 FPs, there should be approximately 4000 total test cases and 1200 acceptance test cases.

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