Correctness vs Robustness

In programming, correctness and robustness are two high-level principles from which a number of other principles can be traced back to.

Correctness Robustness
Design by Contract
Defensive programming
Fail Fast
Populating missing parameters
Sensible defaults
Getting out of the users way
Anticorruption Layer
Backwards-compatible APIs

Robustness adds built-in tolerance for common and non-critical mistakes, while correctness throws an error when it encounters anything less than perfect input. Although they are contradictory, both principles play an important role in most software, so it’s important to know when each is appropriate, and why.


“Robustness” is well known as one of the founding principles of the internet, and is probably one of the major contributing factors to its success. Postel’s Law summarizes it simply:

Be conservative in what you do; be liberal in what you accept from others.

Postel’s Law originally referred to other computers on a network, but these days, it can be applied to files, configuration, third party code, other developers, and even users themselves. For example:

Problem Robust approach Correct approach
A rogue web browser that adds trailing whitespace to HTTP headers. Strip whitespace, process request as normal. Return HTTP 400 Bad Request error status to client.
A video file with corrupt frames. Skip over corrupt area to next playable section. Stop playback, raise “Corrupt video file” error.
A config file with lines commented out using the wrong character. Internally recognize most common comment prefixes, ignore them. Terminate on startup with “bad configuration” error.
A user who enters dates in a strange format. Try parsing the string against a number of different date formats. Render the correct format back to the user. Invalid date error.

(Important side note: Postel’s law doesn’t suggest skipping validation entirely, but that errors in non-essential items simply be logged and/or warned about instead of throwing fatal exceptions.)

In many cases, relentless pursuit of correctness results in a pretty bad user experience. One recent annoyance of mine is companies that can’t handle spaces in credit card numbers. Computers are pretty good at text processing, so wasting the user’s time by forcing them to retype their credit card numbers in strange formats is pure laziness on behalf of the developer. Especially if you consider the validation error probably took more code than simply stripping the spaces out.

Compare this to Google Maps, where you can enter just about anything in the search box and it will figure out a street address. I know which I prefer using.

Robustness makes users’ lives easier, and by doing so promotes uptake. Not only amongst end users, but also developers — if you’re developing a standard/library/service/whatever, and can build in a bit of well thought-out flexibility, it’s going to grant a second chance for users with clients that aren’t quite compliant, instead of kicking them out cold.

Adam Bosworth noted a couple of examples of this in a recent post what makes successful standards:

If there is something in HTTP that the receiver doesn’t understand it ignores it. It doesn’t break. If there is something in HTML that the browser doesn’t understand, it ignores it. It doesn’t break. See Postel’s law. Assume the unexpected. False precision is the graveyard of successful standards. XML Schema did very badly in this regard.

HTTP and HTML are displaying robustness here; if they see anything they don’t recognize they simply skip past it. XML Schema on the other hand fails validation if there are any extra elements/attributes present other than precisely those specified in the XSD.


So robustness makes life easier for users and third-party developers. But correctness, on the other hand, makes life easier for your developers according to the books on software development methodologies — instead of bogging down checking/fixing parameters and working around strange edge cases, they can focus on the one single model where all assumptions are guaranteed. Any states outside the main success path can thus be ignored (by failing noisily) — producing code that is briefer, easier to understand, and easier to maintain.

For example, consider parsing XHTML vs regular HTML. If XHTML isn’t well formed, you can simply throw a parser error and give up. HTML on the other hand has thoroughly documented graceful error handling and recovery procedures that you need to implement — much harder than simply validating it as XML.

Which should you use then?

We have a conflict here between robustness or correctness as a guiding principle. I remember one paralyzing moment of indecision I had when I was a bit younger with this very question. The specific details aren’t important, but the 50/50 problem basically boiled down to this: If my code doesn’t technically require this value to be provided, should I check it anyway?

To answer this question, you need to be aware of where you are in the code base, and who it is serving.

  • External interfaces (UI, input files, configuration, API etc) exist primarily to serve users and third parties. Make them robust, and as accommodating as possible, with the expectation that people will input garbage.
  • An application’s internal model (i.e. domain model) should be as simple as possible, and always be in a 100% valid state. Use invariants and assertions to make safe assumptions, and just throw a big fat exception whenever you encounter anything that isn’t right.
  • Protect the internal model from external interfaces with an anti-corruption layer which maps and corrects invalid input where possible, before passing it to the internal model.

Remember if you ignore users’ needs, no one will want to use your software. And if you ignore programmers’ needs, there won’t be any software. So make your external interfaces robust. Make your internal model correct.

Or in other words: internally, seek correctness; externally, seek robustness. A successful application needs both.

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