Description
As a software defined storage vendor, we put a lot of effort into automated testing for our ActiveScale product. The process for running this battery of test suites is getting more complex, and the investigation of test failures time consuming. We want to adopt a more data driven approach to this test suite execution and analysis, based on the data we already collect (infrastructure metrics, test results, code metrics).

In this internship you will work on a ML/bot that aggregates test execution metrics and interacts with our CI/CD environment. Your main tasks will be:
• Extend our bot implementation with rules that act on collected data and user input (give the bot a brain ☺):
o Rerun test suites in case of infrastructure issues or unstable test results.
o React on user comments (rerun test suite, close pull requests, assign for review).
o Automatically merge in branches from pull requests if tests are green.
o Optimize test suite scheduling.
o Update tickets in the issue tracker with build results.
• Leveraging the bot to further automate and improve our current CI/CD process.
• Use ML techniques to extract information from our collected data.

Technologies
• Jenkins
• Python
• Machine Learning: scikit-learn
• ElasticSearch
• Flask
• gitlab
• Linux