Semesters of Code 2026: Log Analysis Project - adoptium/infrastructure GitHub Wiki

This project proposes the development of an AI-powered log analysis assistant designed to automatically review Jenkins server logs, system logs (such as syslog), and other infrastructure outputs to identify warnings, errors, and anomalous patterns in real time. By leveraging modern machine learning and natural language processing techniques, the tool will intelligently parse large volumes of unstructured log data, prioritise issues based on severity and historical impact, and correlate related events across multiple sources to provide meaningful insights rather than raw noise.

Beyond detection, the system will suggest potential root causes and actionable remediation steps by mapping observed failures to known issue patterns, documentation, and prior resolutions within the project ecosystem. The solution will integrate seamlessly into existing CI/CD pipelines and infrastructure environments, offering both automated reporting and on-demand analysis via a simple interface or API, ultimately reducing manual debugging effort, improving system reliability, and enabling faster incident response for large-scale distributed systems such as those used in Adoptium.

Mentors: Scott Fryer, Haroon Khel, Andrew Leonard, Shelley Lambert

Students: Samuel Yuan, Marcus White, Faisal Toosan, Yuchen Zhou, Hani Murtaza, Aashka Shah

Related GitHub EPICS/issues: https://github.com/orgs/adoptium/projects/48/views/16