On Sat, Oct 25, 2008 at 9:29 AM, Vladimir Nesov <[EMAIL PROTECTED]> wrote:
> There are systems that do just that, constructing models of a program
> and representing conditions of absence of a bug as huge formulas. They
> work with various limitations, theorem-prover based systems using
> counterexample-driven abstraction refinement (the most semantically
> accurate brute force models) able to work with programs of up to about
> tens of thousands lines of code. They don't scale. And they don't even
> handle loops well. Then there are ways to make anaylsis more scaleable
> or precise, usually in a tradeoff. The most of what used to be AI that
> enters this scene are theorem provers (that don't promise to solve all
> the problems), and cosmetic statistical analyses here and there.

Thanks, that was about where I understood the state-of-the-art to be.

> What I see as potential way of AI in program analysis is cracking
> abstract interpretation, automatically inventing invariants and
> proving that they hold, using these invariants to interface between
> results of analysis in different parts of the program and to answer
> the questions posed before analysis. This task has interesting
> similarities with summarizing world-model, where you need to perform
> inference on a huge network of elements of physical reality (start
> with physical laws, if they were simple, or chess rules in a chess
> game), basically by dynamically applying summarizing events, matching
> simplified models.

Yes, that's the sort of thing I have in mind.

> But it all looks almost AI-complete.

It's a very hard problem, but it's a long way short of AI complete. I
think it's worth aiming for as an intermediate stage between the
current state of the art and "good morning Dr. Chandra".


-------------------------------------------
agi
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