Jenkins

Mentors: Student: faheem_128

This project adds OAuth 2.0 support to the Jenkins email-ext plugin, addressing the deprecation of basic SMTP authentication by providers like Microsoft and Google. It integrates with the oauth-credentials framework to securely obtain access tokens and enables XOAUTH2-based SMTP authentication using Jakarta Mail. The solution is provider-agnostic, allowing compatibility with multiple email services. Deliverables include OAuth integration, UI updates for configuration and credential selection, a test email feature, comprehensive test coverage, and full documentation, while maintaining backward compatibility with existing authentication methods.

Mentors: Student: Vatsal Verma

This project aims to migrate the Jenkins.io Success Stories website from Gatsby to Vite, enabling faster builds, improved developer experience, and better performance. It focuses on modernizing the UI/UX with a responsive, accessible, and user-centric design. A key objective is redesigning the story submission workflow by moving from Decap/Netlify CMS to a GitHub Actions–based workflow, allowing structured, automated, and scalable content contributions. This transition will also involve refining and standardizing YAML-based content files for better consistency, validation, and maintainability. Overall, the project enhances performance, scalability, and contributor experience while aligning the platform with modern web development practices.

Mentors: Student: _Pratik Mane

The project aims to build a trusted Jenkins CI pipeline that fetches and consolidates raw metadata from metadata-plugin-modernizer repo into a single structured JSON format, which is published to reports.jenkins.io. It also involves building a static visualization UI site on top of it that consumes this data during its build process to present dashboards and per-plugin reports.

Mentors: Student: Daniele Caldarigi

While Jenkins is the backbone of modern CI/CD, troubleshooting failed builds and complex configurations remains a time-consuming bottleneck. This project develops a Diagnostic AI Chatbot plugin to drastically reduce this friction, minimizing debugging time and maximizing developer productivity. Powered by an advanced Retrieval-Augmented Generation (RAG) architecture, the agent leverages LangGraph and hybrid search to intelligently filter noisy build logs, cross-referencing them with official documentation and community discussions in order to deliver precise root-cause analysis and actionable fixes. Architecturally, it utilizes a decoupled FastAPI backend, ensuring zero computational overhead on the Jenkins Controller. The system is highly flexible: it is designed with a privacy-first approach optimized for local open-source LLMs, while seamlessly supporting integration with third-party commercial APIs. This allows administrators to effortlessly toggle between absolute data privacy and frontier model performance based on their infrastructure needs.

Mentors: Student: Mallikarjun G D

The Jenkins Resources AI Chatbot can answer "how do I install this plugin?" but it can't tell you why your build broke, whether two plugins conflict, or whether last week's prompt change made answers worse. This project fixes that: an LLM-as-a-Judge evaluation pipeline, GraphRAG for relational plugin queries, a Build Failure Diagnosis Agent, and simplifed plugin setup all built directly on top of the existing chatbot-core architecture.