Comparative Performance of Machine Learning Algorithms for Predicting Future Committer in Blockchain Projects

Abstract

The success of blockchain projects depends, to a large extent, on the developer's involvement. Source code-commit privilege to the project is typically restricted to a few developers known as committers. Project leaders continuously search for new committers to evolve into a high-quality blockchain. However, promoting developers into committers is risky, mainly when the promoted committers exhibit low involvement behaviour. Hence, the committer assessment process is critical to the successful evolution of blockchain projects. The phenomenon of developer induction as code committers has previously been explored in the literature. However, previous studies employed objective measures such as the number of bugs report and code patches to appraise the developers activities for promotion purposes. Although these approaches are significant, behavioural tendencies influencing developers' activities are ignored. There have, however, been comparatively few investigations on a subjective measure that evaluate developers' perception of their activities for promotion purposes. This study aims to appraise blockchain developers' perceptions for predicting future committers in blockchain projects. In this study, 173 blockchain developers’ perceptions were gathered. The study employed hybrid analyses such as Structural Equation Modelling (SEM) to first analyse the survey data and Machine Learning (ML) algorithms to predict future committers. The performance of various ML algorithms was evaluated compared to classification performance indicators such as the F1 score, accuracy, precision, and AUC. In addition, the study also investigates the most important factors that predict future committers. The results indicate that XGBoost algorithm achieves the best performance with an accuracy of 0.94. Moreover, the most important factors in predicting future committers in a blockchain project are project desertion, developer involvement, decision-right delegation and system integration.