Experience-Weighted Cognitive Complexity Metric for Software Understandability: A Cognitive-Informatics Perspective

Software systems are becoming increasingly complex as they evolve to meet the growing demands of modern industries. Traditional complexity metrics such as McCabe's Cyclomatic Complexity and Halstead's metrics fail to adequately capture the human cognitive dimension of software comprehension. This paper introduces an Experience-Weighted Cognitive Complexity Metric (EWCCM) based on Cognitive Informatics principles to quantify software understandability through both structural and human-centric parameters. The model extends existing cognitive complexity formulations by integrating an experience factor (Fe) that accounts for programmers' familiarity with programming languages, control structures, and paradigms. Empirical validation using survey data from 100 developers across academia and industry revealed a strong positive correlation (r = 0.97) between programming experience and code comprehension efficiency. The study's findings demonstrate that integrating human experience enhances the accuracy of software quality predictions, particularly for maintainability and readability assessment. The proposed EWCCM advances software engineering measurement theory and supports the development of human-aware tools for code review, education, and automated software analytics.