Thanks for looking at the code, and for your feedback!
Here is a recipe for adding error reporting (reporting of all uncaught exceptions) in a Python project. The highlighted line shows that, when you instantiate a reporter, you have to pass a consent mechanism:
https://github.com/bugout-dev/humbug/blob/main/python/recipe...
We allow you to create a consent mechanism that always returns true:
> consent = HumbugConsent(True)
Of course, someone can always create their own subclass of HumbugConsent which overrides that check. We don't have a good way to prevent this, nor would we want to restrict anyone's freedom to modify code.
Our emphasis is on building simple programs that we can reasonably expect to run on any reasonable client without using an exorbitant amount of CPU or memory. For this reason, we aren't using black box ML models. Rather, we analyzed the data and came up with some simple regex based rules on how to deidentify stack traces for our v1 implementation.
We are in the process of doing this for more languages and building this into a proper deidentification library that can be imported into any runtime - Python + Javascript + Go + etc.
Here is a recipe for adding error reporting (reporting of all uncaught exceptions) in a Python project. The highlighted line shows that, when you instantiate a reporter, you have to pass a consent mechanism: https://github.com/bugout-dev/humbug/blob/main/python/recipe...
We allow you to create a consent mechanism that always returns true: > consent = HumbugConsent(True)
But even with that mechanism, we ultimately respect BUGGER_OFF=true: https://github.com/bugout-dev/humbug/blob/main/python/humbug...
Of course, someone can always create their own subclass of HumbugConsent which overrides that check. We don't have a good way to prevent this, nor would we want to restrict anyone's freedom to modify code.
re: Kaggle and stack trace deidentification
We started by crawling public GitHub issues for Python stack traces and built up a decent sized dataset of these: https://www.kaggle.com/simiotic/python-tracebacks
Our emphasis is on building simple programs that we can reasonably expect to run on any reasonable client without using an exorbitant amount of CPU or memory. For this reason, we aren't using black box ML models. Rather, we analyzed the data and came up with some simple regex based rules on how to deidentify stack traces for our v1 implementation.
We are in the process of doing this for more languages and building this into a proper deidentification library that can be imported into any runtime - Python + Javascript + Go + etc.
Apologies for the link not working. It seems I had to publish a version of the notebook. This link should work now: https://www.kaggle.com/simiotic/python-tracebacks-redactor
Actually, we started this work on a livestream if you're interested in watching: https://youtu.be/TFKe614Ml1M
Again, really appreciate your engagement and feedback. Thank you!