We have this:
The popular tab queue is currently the result is a (pseudo-)random selection from all images with labels awaiting review. It currently does not prioritize assessed images (featured/quality/valued) as originally intended.
We want this:
Explicitly prioritize the assessed images. The short-term way to do this is sort the queue by "suggestion timestamp" (mvs_timestamp in the db) since the assessed images were run through the Machine Vision algorithm first.
In the medium-to-long term, the data model should be updated to include the concept of which pool ("popular" or "uploads") a set of suggestions belongs to.
Screenshots (if possible):
Acceptance Criteria:
- The Popular tab shows only Assessed images until that list has been exhausted (>200k files)
COVID-19 Deployment Criteria (see responses below)
- Can you roll back this change without lasting impact?
- A recovery plan is required as this will help identify our capacity for recovering from the failure
- THIS IS A KEY QUESTION, if you can’t answer it, you shouldn’t deploy
- Is specialized knowledge required to support this change in production? If so, are there multiple people with this knowledge?
- Is there a way to increase confidence about the correctness of this change?
- Reviews (Design, Code, etc)
- Testing coverage (unit tests, integration tests)
- Manual testing (e.g. Beta, vagrant, docker)