A Preliminary Analysis of Software Metrics in Decentralised Applications

Abstract

This study examines software metrics in decentralized applications (dApps) to analyze their structural and behavioral characteristics as they grow in complexity. Sixty dApps were categorized into Small (3 to 29 contracts), Medium (30 to 46 contracts), and Large (47 to 206 contracts) based on their contract count. Initial analysis showed a non-normal data distribution, leading to the use of Spearman's correlation method. Findings revealed that Medium dApps have strong correlations between metrics like 'Average Local Variables' and 'Maximum Local Variables', while Large dApps show higher correlations between 'Number of Functions' and 'State Variable Count', indicating more complex contract structures. The higher Coupling Between Objects (CBO) in large dApps suggests increased interactions with other contracts or libraries, potentially elevating security risks. These insights are valuable for developers and stakeholders in the blockchain and IoT sectors, aiding in understanding how dApps evolve with increasing complexity and the implications on software metric relationships.