I'm looking for guidance on the current industry standards for implementing and enforcing the Design-by-Contract (DbC) paradigm in Python. I came across a PEP 316 article from 2003 that proposed Eiffel-style DbC features, but these haven't been implemented yet. Given that Python isn't typically used for critical systems where correctness is essential, many developers still rely on it for projects where any error is unacceptable. As a freelance data analyst and machine learning engineer, I can't create a proof of correctness for every project, especially since it's often done by teams of seasoned mathematicians. What are the next-best methods to ensure that my Python programs won't run into unexpected issues?
5 Answers
Honestly, Python's approach to Design-by-Contract isn't worse than other languages. You can use libraries like `icontract` or `deal` to help implement DbC principles. It's just that it hasn't really gained traction in the industry yet, unfortunately.
At the end of the day, rigorous testing is key. Eliminate uncertainty by using strongly-typed data structures and validating with Pydantic. That's how you can ensure your program is robust and minimizes unexpected issues.
I agree! Testing is crucial, and using strong data validation can really help.
You can also represent states with types and unions, and use tools like mypy or pyright to validate your types. Make sure to set your CI to fail if there are type checker errors. That's a solid method to enforce correctness.
One way to think about Design-by-Contract these days is to focus on making invalid states impossible to represent in your code. Instead of relying just on contracts, try structuring your code to reinforce these constraints inherently.
To boost your confidence in avoiding unexpected program behaviors, consider using enforced type hints along with type checking. You can leverage Protocols and Abstract Base Classes as strong tools in this area.
Thanks for the suggestions! I'll definitely check out those libraries.