Software Cognitive Information Measure based on Relation Between Structures
This work addresses software engineers and researchers by providing a novel approach to quantify code understandability, though it appears incremental as it builds on existing cognitive complexity measures.
The paper tackles the problem of measuring cognitive complexity in software by proposing a new measure called Structured Cognitive Information Measure (SCIM) based on Granular Computing, which unifies complexity factors to mimic human cognitive processes, and it includes theoretical validation through nine Weyuker's properties.
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. 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. However, according to the methodology of software and the scope of the variables, Information Complexity Number(ICN) of variables is depended on change of variable value and cognitive complexity is measured in several ways. In this paper, we define the Scope Information Complexity Number (SICN) and present the cognitive complexity based on functional decomposition of software, including theoretical validation through nine Weyuker's properties.