Jenkins project

Mentors: Student: stellargo

<p>File fingerprinting is a way to track which version of a file is being used by a job/build, making dependency tracking easy. The fingerprint engine of Jenkins can track usages of artifacts, credentials, files, etc within the system. It does this by maintaining a local XML-based database. This leads to dependence on the physical disk of the Jenkins master. Hence, the core idea of this project is to extend Jenkins core to support storing of fingerprints in an external storage, which would be managed by storage system specific plugins. This would involve designing and creating the external fingerprint storage API. The API would also allow multiple Jenkins instances to store their fingerprints inside a single external storage instance, enabling traceability of artifacts across the CI/CD flow. The project also involves creating two plugins, as reference implementations. These plugins would allow users to store fingerprints inside their Redis and PostgreSQL instances.</p>

Mentors: Student: Kezhi Xiong

<p>The GitHub Checks API allows developers to report the CI integrations’ detail information rather than binary pass/fail build status on GitHub pages. This project is about implementing this API as a new Jenkins plugin for other plugins. By consuming this API, they can easily create GitHub checks. Thus, any information during the Jenkins CI process like warnings, durations, summaries, and conclusions can be directly shown on GitHub pages.</p>

Mentors: Student: Buddhika Chathuranga

<p>Enhance Jenkins server and client service management on Windows by offering new configuration file formats and improving settings validation. Currently, the configuration file is written in XML and there are fewer validations. In this project, I will convert the configurations file into YAML and add validations for configurations.</p>

Mentors: Student: Loghi Perinpanayagam

<p>This project provides a plugin for data scientists to integrate Machine Learning Workflow with Jenkins. The plugin will be a golden solution for the current problems not solvable by existing notebooks in the Data Science world. It helps to the Jenkins community to make an impact on Machine Learning history. Each ML steps can be integrated smoothly with the help of Jenkins. This project mainly focuses on Python, but in the future, it can be extended to other interpreters too.</p>

Mentors: Student: Rishabh Budhouliya

<p>At this moment, the number of installations for git-plugin is 200,000+ and git-client-plugin has a similar usage. These two plugins have one of the most extensively used implementations of SCM API and performance improvement is a much-needed task which is going to affect a broad Jenkins audience.</p> <p>In this project, I wish to use the principles of micro-benchmarking to create and execute a test suite which involves comparison of GitClient APIs implemented by CliGitAPIImpl and JGitAPIImpl using “average execution time per operation” as a performance metric. The results from this study can be used to prioritize implementation for a particular operation and provide an option to the users to improve end-to-end performance of the plugins.</p> <p>Apart from benchmarking, I wish to resolve the JENKINS-49757 issue. Currently, git checkout step calls git fetch twice, where the second fetch is an incremental call. Resolution of this issue will improve git plugin performance.</p> <p>Also, by running JUnit tests with code coverage, I wish to identify and create more automated test cases which cover the implementations in git-client-plugin and their functionalities in the git-plugin.</p>