Examining the Software Developers’ Perception in Open-Source Software of Blockchain Project Using Association Rules Mining

Developers are vital in enhancing blockchain projects like Bitcoin through feature additions, bug fixes, and performance optimization. However, comprehending developers’ perception of the growing amount of information regarding new features and bug fixes becomes challenging as blockchain projects gain popularity. Data mining, a technique that extracts valuable patterns and information from extensive data sets, assists in decision-making. The Apriori algorithm, widely used in data mining, uncovers association rules among sets of items. Despite its effectiveness, the Apriori algorithm remains relatively underutilized in the field of Open Source Software (OSS) developer turnover. Previous studies have employed approaches like mining software project repositories, social network analysis, quantitative data analysis, and surveys, which shed light on turnover but fail to reveal interesting relationships and patterns related to subjective factors and collaboration. To address this gap, this paper proposes combining survey data with association rule mining. This approach aims to identify co-occurrence patterns between specific personal and project-related variables (e.g., intention to learn or system integration). By analyzing these variables and their associations, the paper intends to provide valuable insights to project leaders, aiding decision-making in developer turnover management. Ultimately, this research contributes to enhancing the quality of blockchain projects.