The academic programming language community spends lots of time studying and building type systems. It’s widely considered that soundness is the mark of a well designed type system. A sound type system never permits an incorrectly typed program to pass type checking. An ideal type system would also be complete; it would never reject a correctly typed program because it was unable to prove its correctness. If you’re not familiar with this terminology, Ben Karel offers an illuminating analogy to help you.
Recently, a flurry of new, statically typed languages have entered the public stage. Among these are Google’s Dart, as well as Microsoft’s TypeScript. One thing that these languages have in common is that they have unsound type systems. Cue the horrified gasps of the type theorists!
Ignoring the topic of completeness for a moment, since most critics do so themselves (I think “unsound” just sounds scarier than “incomplete”). Surely Google and Microsoft employ some smart folks who have followed the latest research in type systems, so why, in 2014, are we still building languages that have unsound type systems? Well, I’ll let the Googlers and Microsofties explain themselves. In short, it’s difficult to maintain soundness in feature-rich languages. Even when possible, it may cause an astronomical increase in complexity. Achieving soundness requires a trade off against completeness, complexity, toolability, and more. Never mind the fact that being fully sound isn’t even that useful to begin with!
Let’s step back and examine why we even bother with type systems at all. In many cases, static analysis can dramatically reduce programmer errors and opens up significant optimization opportunities. Programming languages that are amenable to formal static analysis are highly correlated with programming languages that are amenable to informal static analysis, i.e. by programmers! Type systems have proven to be a particularly useful category of static analysis; so much so, that many programmers can’t imagine building software without a type checker on hand at all times.
Sadly, type systems fundamentally can’t catch all kinds of programming mistakes, nor will they magically improve the time or space complexity of your algorithms. You’ll have to write tests and benchmarks either way, to say nothing of validating your designs with stakeholders! Still, there are other reasons to have a good type system, like enforcement of type safety (which is a different thing than “type correctness”). However, safety can also be enforced by dynamic means and dynamic mechanisms such as capabilities can enforce security far beyond that of static safety checks. You might even want to intentionally forego safety in order to achieve maximum performance. Furthermore, there’s lots of other kinds of static analysis, both formal and informal, that can be used to find bugs or speed your programs up.
Assuming we want a tool that finds as many errors as possible, we should be willing to forego completeness, instead preferring to have our static analysis be overly cautious, warning us of programs that might be incorrect. We can always suppress such warnings after informal analysis (or alternative formal analysis!). Assuming we’re realistic about the fact that no static analysis tool will ever catch all errors, we should also be willing to tolerate unsoundness in order to spend our mental complexity budgets more wisely. Assuming we want a tool that independently improves with time, we should not create a cyclic dependency between languages and type systems.
There is a class of such tools. They’re called linters.