Manage 10s or 100s of release pipelines uniformly using a data pipeline approach ("mutators"). Testing for uniformity of release pipelines in the areas which matter, as defined by the user in the tests, by using PyTest.
Used during a migration of 93 discrete applications and libraries from Jenkins + (on-prem) VM deployment approach to use Azure Pipelines + (on-prem) Kubernetes. This ensured uniformity among all 93 applications while still allowing for unique patterns among a subset of applications.
Platform🔗
- Azure DevOps Release Pipelines
- Python 3.8
- PyTest
Features🔗
- Mutators make surgical changes to each pipeline passed, getting latest copy at start and uploading changed copy at the end
- Tests include business-specific requirements including, but not limited to:
- Who is permitted to approve deployment to a given environment
- Order in which to promote the build artifact to each environment
- Details of each task in deploying to each environment (for uniformity)
- Library of helper functions to make tests more readable (e.g.,
is_bash_task()compares GUID of task object with known GUID for Bash task)
Obstacles🔗
- Opaque or incomplete API documentatation, relying on the official Python SDK to fill in the gaps
- Reverse engineering undocumented (public) APIs for Secure Files interactivity