KubeCon + CloudNativeCon | Open Source Summit China 2019

Massive amounts of test logs and console output are generated each day at the AdoptOpenJDK and Eclipse OpenJ9 projects because of the huge number of tests multiplied by the number of versions, platforms and implementations tested. This 'noise' from daily, personal, and pull request builds require processing in order to consume and make sense of it. In some cases, we can instrument the test code to be less verbose. In all cases, this raw data need to be refined effectively to understand what next action to take.

This presentation covers our approaches to the application of data visualization, filtering, and categorization. We are also trying to employing relevant technologies such as deep learning. Our ultimate goal is to display the results efficiently, allow users to quickly narrow down the problems and better monitor the farm to support the complex continuous delivery pipelines.