What are different metrics for testing?

Software testers must rely on analysis, design, and code metrics to guide them in design and execution of test cases. Metrics for testing fall into two broad categories:
- metrics that attempt to predict the likely number of tests required at various testing levels.
- metrics that focus on test coverage for a given component.

Function based metrics use a predictor for overall testing effort. Architectural design metrics provide information on the ease and difficulty which is associated with integration testing.

The metrics defined for object oriented provide a general indication of the amount of testing effort required to exercise an object oriented system. Object oriented testing can be quite complex. Metrics can assist in targeting testing resources at threads, scenarios, and packages of classes that are suspect based on measured characteristics. Design metrics that has direct influence on test-ability of object oriented system include:

- Lack of cohesion in methods (LCOM): Higher the value of LCOM, more states must be tested.
- Percent public and protected (PAP): High value of PAP increases the possibility of side effects among classes because public and protected attributes lead to high coupling.
- Public access to data members (PAD): High value of PAD increases the possibility of side effects among classes.
- Number of root causes (NOR): NOR is the count of distinct class hierarchy described in design model. As NOR increases, testing effort increases.
- Fan-in (FIN): It is an indication of multiple inheritance. If it is greater than 1, a class inherits its attributes and operations from more than one root class.
- Number of children (NOC) and depth of inheritance tree (DIT): The super class methods will have to be retested for each sub class.