Information flow metrics and complexity measurement

Abstract

Complexity metrics takes an important role to predict fault density and failure rate in the software deployment. Information flow represents the flow of data in collective procedures in the processes of a concrete system. The present static analysis of source code and the ability of metrics are incompetent to predict the actual amount of information flow complexity in the modules. In this paper, we propose metrics IF-C, F-(I+O), (C-L) and (P-C) focuses on the improved information flow complexity estimation method, which is used to evaluate the static measure of the source code and facilitates the limitation of the existing work. Using the IF-C method, the framework of information flow metrics can be significantly combined with external flow of the active procedure, which depends on various levels of procedure call in the code.