Researchers have used information flow metrics between modules. For understanding the measurement, let us understand the way the data moves through a system:

• Local direct flow exists if

  (i) A module invokes a second module and passes information to it.

  (ii) the invoked module returns a result to the caller.

• Local indirect flow exists if the invoked module returns information that is subsequently passed to a second invoked module.

• Global flow exists if information flows from one module to another via a global data structure.

The two particular attributes of the information flow can be described as follows:

(i) Fan-in of a module m is the number of local flows that terminates at m, plus the number of data structures from which information is retrieved by m.

(ii) Fan-out of a module m is the number of local flows that emanate from m, plus the number of data structures that are updated by m.

Henry and Kafura Deign Metric

Henry and Kafura's information flow metric is a well-known approach for measuring the total level of information flow between individual modules and the rest of the system. They measure the information flow complexity as

$I F C(m)=\operatorname{length}(m) \times\left((f a n-i n(m) \times f a n-o u t(m))^{2}\right.$

Higher the $I F$ complexity of $m,$ greater is the effort in integration and integration testing, thereby increasing the probability of errors in the module.