Towards Structured Software Cognitive Complexity Measurement with Granular Computing Strategies

Cognitive complexity measures quantify human difficulty in understanding the source code based on cognitive informatics foundation. The discipline derives cognitive complexity on a basis of fundamental software factors i.e. inputs, outputs, and internal processing architecture. The invention of Cognitive Functional Size (CFS) stands out as the breakthrough to software complexity measures. Several subsequent research has tried to enhance CFS to fully consider more factors, such as information contents in the form of identifiers and operators. However, these existing approaches quantify the factors separately without considering the relationship, among them. This paper presents an approach to integrating Granular Computing into the new measure called Structured Cognitive Information Measure or SCIM. The proposed measure unifies and re-organizes complexity factors analogous to human cognitive process. Empirical studies were conducted to evaluate the virtue of SCIM, including theoretical validation through nine Weyuker's properties. The universal applicability of granular computing concepts is also demonstrated.