Towards a Structural Equation Model of Open Source Blockchain Software Health

TL;DR

This paper develops a structural equation model to assess open source blockchain software health, integrating interest, engagement, and robustness using GitHub data and exploratory factor analysis.

Contribution

It introduces a novel structural model linking public interest, developer engagement, and software robustness in blockchain projects.

Findings

Interest is driven by stars, forks, and mentions

Robustness correlates with criticality, update recency, rank, and geography

Interest positively influences engagement, which enhances robustness

Abstract

The widespread use of GitHub among software developers as a communal platform for coordinating software development has led to an abundant supply of publicly accessible data. Ever since the inception of Bitcoin, blockchain teams have incorporated the concept of open source code as a fundamental principle, thus making the majority of blockchain-based projects' code and version control data available for analysis. We define health in open source software projects to be a combination of the concepts of sustainability, robustness, and niche occupation. Sustainability is further divided into interest and engagement. This work uses exploratory factor analysis to identify latent constructs that are representative of general public interest or popularity in software, and software robustness within open source blockchain projects. We find that interest is a combination of stars, forks, and text mentions in the GitHub repository, while a second factor for robustness is composed of a criticality score, time since last updated, numerical rank, and geographic distribution. Cross validation of the dataset is carried out with good support for the model. A structural model of software health is proposed such that general interest positively influences developer engagement, which, in turn, positively predicts software robustness. The implications of structural equation modelling in the context of software engineering and next steps are discussed.