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I'm trying to write a highly-reliable piece of code in Python. The most common issue I run into is that after running for a while, some edge case will occur and raise an exception that I haven't handled. This most often happens when using external libraries - without reading the source code, I don't know of an easy way to get a list of all exceptions that might be raised when using a specific library function, so it's hard to know what to handle. I understand that it's bad practice to use catch-all handlers, so I haven't been doing that.
Is there a good solution for this? It seems like it should be possible for a static analysis tool to check that all exceptions are handled, but I haven't found one. Does this exist? If not, why? (is it impossible? a bad idea? etc) I especially would like it to analyze imported code for the reason explained above.
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An unhandled exception displays an error message and the program suddenly crashes. To avoid such a scenario, there are two methods to handle Python exceptions: Try – This method catches the exceptions raised by the program. Raise – Triggers an exception manually using custom exceptions.
If your application has unhandled exceptions, that may be logged in the Windows Event Viewer under the category of “Application”. This can be helpful if you can't figure out why your application suddenly crashes. Windows Event Viewer may log 2 different entries for the same exception.
There are two ways you can use assertRaises: using keyword arguments. Just pass the exception, the callable function and the parameters of the callable function as keyword arguments that will elicit the exception. Make a function call that should raise the exception with a context.
"it's bad practice to use catch-all handlers" to ignore exceptions:
Our web service has an except which wraps the main loop.
except:
log_exception()
attempt_recovery()
This is good, as it notifies us (necessary) of the unexpected error and then tries to recover (not necessary). We can then go look at those logs and figure out what went wrong so we can prevent it from hitting our general exception again.
This is what you want to avoid:
except:
pass
Because it ignores the error... then you don't know an error happened and your data may be corrupted/invalid/gone/stolen by bears. Your server may be up/down/on fire. We have no idea because we ignored the exception.
Python doesn't require registering of what exceptions might be thrown, so there are no checks for all exceptions a module might throw, but most will give you some idea of what you should be ready to handle in the docs. Depending on your service, when it gets an unhandled exception, you might want to:
Notice a trend? The action changes, but you never want to ignore it.
119
Great question.
You can try approaching the problem statically (by, for instance, introducing a custom flake8 rule?), but, I think, it's a problem of testing and test coverage scope. I would approach the problem by adding more "negative path"/"error handling" checks/tests to the places where third-party packages are used, introducing mock side effects whenever needed and monitoring coverage reports at the same time.
I would also look into Mutation Testing idea (check out Cosmic Ray Python package). I am not sure if mutants can be configured to also throw exceptions, but see if it could be helpful.
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