Coming from a self taught dev who started in the late 90s, I don’t feel any need for ML in my work testing or otherwise. But source control, automated testing, and other computerized validation have been godsends even if they made me weep on introduction.
And if I were building anything with the scope and scale of Firefox, I would be reaching for every possible testing advancement I could find, at least to evaluate.
I’m already investigating property and mutation testing tools for hobby projects because I want to use them for real world things and want to be able to recommend them to friends, and hopefully eventually others. And because their scopes are large once applied, even if the code I’ve written is small.
The reality is that “does it compile” is great for personal projects but falls apart the moment it’s on a network, even a sneakernet.
I think ML is just an extension of test coverage and static analysis tools.
We already went in the realm of "this looks bad, you should check this block" warnings with CI tools searching for security vulnerabilities and bad coding practices, and personally I found it to help tremendously, making the traditional hand written test cases less useful in some ways. Going further to optimize the process seems a non brainer.
And if I were building anything with the scope and scale of Firefox, I would be reaching for every possible testing advancement I could find, at least to evaluate.
I’m already investigating property and mutation testing tools for hobby projects because I want to use them for real world things and want to be able to recommend them to friends, and hopefully eventually others. And because their scopes are large once applied, even if the code I’ve written is small.
The reality is that “does it compile” is great for personal projects but falls apart the moment it’s on a network, even a sneakernet.