(Whether the number itself is a real account is a different matter that would be handled elsewhere through a database query.) In this example, again, the function should return true for only properly formatted strings. It's especially important to test security cases like injection attacks if the validated string is later used in database queries or displayed in the app's UI.įor each input, you then define the function's expected return value (or values). To thoroughly test this function, you want to throw at it every conceivable input: valid strings, mistyped strings (off by one or two characters, or containing invalid characters), strings that are too short or too long, blank strings, null arguments, strings containing control characters (non-text codes), string containing HTML, strings containing injection attacks (such as SQL commands or JavaScript code), and so on. In this example, the function accepts any string and returns true if that string contains a properly formatted account number, false otherwise. Unit tests are concerned only with the unit's interface-its arguments and return values-not with its implementation (which is why no code is shown here in the function body often you'd be using other well-tested libraries to help implement the function). Unit tests are then other pieces of code that specifically exercise the code unit with a full range of different inputs, including boundary and edge cases.įor example, say you have a function to validate the format of an account number that a user enters in a web form: def validate_account_number_format ( account_string ): # Return False if invalid, True if valid #.

(If you're already familiar with unit testing, you can skip to the walkthroughs.)Ī unit is a specific piece of code to be tested, such as a function or a class.

The Python extension supports testing with Python's built-in unittest framework and pytest. Configure IntelliSense for cross-compilingĮdit Python testing in Visual Studio Code.

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