Information Flow Metrics
The other set of metrics we would live to consider are known as Information Flow Metrics. The basis of information flow metrics is found upon the following concept the simplest system consists of the component, and it is the work that these components do and how they are fitted together that identify the complexity of the system. The following are the working definitions that are used in Information flow:
Component: Any element identified by decomposing a (software) system into it’s constituent’s parts.
Cohesion: The degree to which a component performs a single function.
Coupling: The term used to describe the degree of linkage between one component to others in the same system.
Information Flow metrics deal with this type of complexity by observing the flow of information among system components or modules. This metrics is given by Henry and Kafura. So it is also known as Henry and Kafura’s Metric.
This metrics is based on the measurement of the information flow among system modules. It is sensitive to the complexity due to interconnection among system component. This measure includes the complexity of a software module is defined to be the sum of complexities of the procedures included in the module. A process contributes complexity due to the following two factors.
- The complexity of the procedure code itself.
- The complexity due to the procedure’s connections to its environment. The effect of the first factor has been included through LOC (Line Of Code) measure. For the quantification of the second factor, Henry and Kafura have defined two terms, namely FAN-IN and FAN-OUT.
FAN-IN: FAN-IN of a procedure is the number of local flows into that procedure plus the number of data structures from which this procedure retrieve information.
FAN -OUT: FAN-OUT is the number of local flows from that procedure plus the number of data structures which that procedure updates.
Procedure Complexity = Length * (FAN-IN * FANOUT)**2
