Sure. I'll go ahead and assume the use-case will be good with "basic" - that sounds pretty fundamental to me.
The following are from the "basic test case template" column of this site.
To test the effectiveness of personalized search results, we can use a variety of helpful methods:
- First of all, we want to maintain the existing user experience. Performing research on what elements of the current UX users value may assist in defining the test case. However, given that we don't have the ability to do that here, let's move on.
- Another important factor lies in responsiveness. Adding additional elements to a search query may increase the amount of time users need to wait until they can interact with the system again. We can test this fairly easily given a reproducible environment. It can also act as a regression test for future changes!
- We should also attempt to measure the relevance of returned results. A fun test here might be comparing existing (both traditional and machine learning) algorithms with any new generative AI, if applicable. Generally, the new feature shouldn't stray too far from more typical personalization methods.
There are others, but most require lots of specific details. As such, I'll focus on the second potential test case: responsiveness.
- Reproducible environment
- Representative sample of typical elements/results to search on
- A collection of common searches
- Mock accounts with lots of detail and usage history for the algorithm to use
N/A. (the use case is fictional)
N/A.
This test case verifies the responsiveness of the modifed, personalized search system, ensuring that the newest changes do not affect reactivity in the program.
Set this up using these guidelimes:
- Setup a reproducible environment to run the tests in. Virtualization/containers will work great here!
- Fill the database of results with reasonable attributes - use real elements if possible (i.e. legal).
- Create a collection of mock accounts with fake usage histories.
- Use a collection of common searches.
- Run each of the queries on each account.
- Cache info about results, but more importantly, reactivity.
We can now run the test by following these steps:
- Grab the most recent cache timings.
- For each account, run each search on it.
- Grab the reactivity timings (such as wait time, etc.) and compare them with the previous cached timings.
- If the new timings are significantly longer, we have caught a regression. Fail the test.
- Otherwise, cache the new timings and pass the test.
Now we have a regression test about the search result reactivity. So that's neat.
Roger.