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If you modify a complex piece of code, and your tests pass on the first try, you should immediately proceed to break the code in an obvious way and rerun the tests, to check that you're actually testing what you think you're testing.
The machine learning equivalent: shuffle the labels in your test dataset (so they don't match the test inputs anymore, while keeping the same class distribution) and rerun evaluation. If your accuracy is still as high as before, you have a problem
The best accuracy achievable without looking at the inputs (i.e. just by learning the label distribution) can be a pretty good initial baseline for a difficult problem.
Mutation Testing?
great suggestion. Sometime the smoke tests we run was not covering the part of code we changed!! And we think everything is ok as test passed. Knowing the correct tests to run is an art!
Automate this w QuickCheck https://en.wikipedia.org/wiki/QuickCheck generally property based testinghttps://twitter.com/daniel_bilar/status/1363540291228205059 …
This is why coverage reports are so useful
Even just reference labels in the IDE can be useful for that, but those and code coverage tools won't necessarily pick up cases where there's certain conditions not being exercised.
subtweet? :)
That’s actually very unintuitive and good advice. Thanks!
There is nothing more suspicious than code that works on the first try!
Oh boy, I hope it compiles and works!!
Okay... what the f is this aboutpic.twitter.com/0KviR9ee3W
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