A Methodology for Analysing Code Anomalies in Open-Source Software Using Big Data Analytics

This study proposes a methodology aimed at examining the evolution and incidence of code anomalies within open-source software (OSS) projects, analysing their correlation with various development practices and project characteristics. By leveraging big data analytics techniques on version control histories, the proposed research intends to elucidate patterns in anomaly introduction, persistence, and resolution over time, while examining their potential relationships with project metrics, such as activity levels, contributor demographics, and commit typologies. The anticipated outcomes are expected to inform the design of developer tools that facilitate proactive anomaly detection and foster sustained code quality management in OSS.