The Correlation among Cognitive Complexity Metrics in Algorithm Analysis

Abstract

In the early stage of software development, design complexity metrics are considered as useful indicators of a software testing effort and quality attributes. However, existing works made great efforts in establishing standardized metrics to evaluate the complexity of software, but there have not been significant efforts in finding the correlations among the cognitive complexity
metrics. To address this challenge, this paper reviewed cognitive complexity metrics which includes: Improved Cognitive Complexity Measure (ICCM), New Cognitive Complexity of Program (NCCoP) and Modified Cognitive Complexity Measure (MCCM). The metrics were employed to analyse some selected sorting algorithms implemented in a procedural C programming language. The relationships among the aforementioned metrics were calculated using the Pearson Correlation Coefficient Method. The results of the comparative examination of ICCM, NCCoP and MCCM revealed that ICCM had more responsive measurements and that there exists a strong relationship among the specified metrics. ICCM had the strongest significance among the considered metrics based on the  efforts in comprehending the information contained in the sorting algorithm codes. The study contributed significantly to understanding and addressing the complexity emanating from software development.