And for the kind of security this article is talking about, it's not enough to have a proven core algorithm or protocol; your dumb input parsers and output renderers and all that other boring integration code also needs to be bug-free, because those kinds of joinery are where we tend to find vulnerabilities.
2. If you use the model checker, it's not about it "failing to find" a counter example. A model checker result is a proof that no such example exists. True, it is usually a proof about some finite instance of your system description.
3. There is just no such thing as proving a system flawless. Software systems are physical systems exhibiting probabilistic behaviors. At best you can prove things about a mathematical description of a system, which mean that as long as the physical system behaves according to the description, the result follows. Of course, the first part at best holds with some probability.
Correctness might well be defined solely by a set of invariants, so I don't quite understand your point here?
So you start writing a model, and a set of invariants, you run the tool and... it finds nothing. Does that mean you just proved the bug does not exist? No, you _know_ the bug exists, what it means is that you didn't write a correct model on your first try. So you iterate on it, add a few things you missed, put some extra labels, read the docs a bit more, maybe refine your invariants, make them stronger, work through a couple traces that don't actually make sense in practice (e.g. actors just never taking any action whatsoever). During all this time, you either found nothing or things that were not interesting. At no time have you ever proved anything correct, you just iterated on your model until you managed to capture a real life scenario.
It takes some convincing to make the jump from "this model does not violate my invariants" to "I think this model and the invariants are close enough to what I care about that I think it's good enough"... I don't know how to go from there to things like "proven correct" and "flawless".
1. \A z \in Zones: Active(z) => ~\E o \in Zones: z # o /\ Active(o) 2. \A w \in Workers: \A z \in Zones: WorkerWantsZone(w, z) ~> WorkerHasZone(w, z)
Unlike your scenario, I am checking the spec here. I know that code that properly implements the spec satisfies these two properties, therefore I've proven the spec correct. You can disagree on the importance of those two properties but I've mechanically checked that those properties are always preserved in my finite models, so I know the spec is correct, at least for the model size.
You know, it's really not too hard to get started with this stuff. You can always nit-pick but why not try it out and see if it's useful for you?
(This is of course assuming that TLC doesn't have any bugs that would cause it to misreport. By contrast something like randomized testing doesn't guarantee the spec satisfies the invariants even if the tester is bug-free.)
With more work, I could prove the invariants for any number of workers and zones using TLAPS rather than TLC, though I couldn't prove the temporal properties in this way (since TLAPS doesn't support temporal logic, it can only prove invariants.)
For instance, this week I needed to build a distributed locking mechanism so a collection of asynchronous actors could only access resources in one region at a time. I described my two requirements in TLA+: only one zone at a time, and everyone who wants a zone eventually gets it (with the assumption nobody goes down forever - weak fairness.) I had several bugs which it caught, until I finally arrived at a protocol that was proven correct (at least for a handful of workers on a handful of zones.) It took around a day from start to finish, and now I know my design is literally flawless.
Sure, I didn't prove it in the general case, nor did I prove that my code implements the spec, but this was still extremely useful! Having the machine check your designs frees you from worrying if you've caught all the races and corner cases.