28,706 research outputs found
Bounded Quantifier Instantiation for Checking Inductive Invariants
We consider the problem of checking whether a proposed invariant
expressed in first-order logic with quantifier alternation is inductive, i.e.
preserved by a piece of code. While the problem is undecidable, modern SMT
solvers can sometimes solve it automatically. However, they employ powerful
quantifier instantiation methods that may diverge, especially when is
not preserved. A notable difficulty arises due to counterexamples of infinite
size.
This paper studies Bounded-Horizon instantiation, a natural method for
guaranteeing the termination of SMT solvers. The method bounds the depth of
terms used in the quantifier instantiation process. We show that this method is
surprisingly powerful for checking quantified invariants in uninterpreted
domains. Furthermore, by producing partial models it can help the user diagnose
the case when is not inductive, especially when the underlying reason
is the existence of infinite counterexamples.
Our main technical result is that Bounded-Horizon is at least as powerful as
instrumentation, which is a manual method to guarantee convergence of the
solver by modifying the program so that it admits a purely universal invariant.
We show that with a bound of 1 we can simulate a natural class of
instrumentations, without the need to modify the code and in a fully automatic
way. We also report on a prototype implementation on top of Z3, which we used
to verify several examples by Bounded-Horizon of bound 1
Sciduction: Combining Induction, Deduction, and Structure for Verification and Synthesis
Even with impressive advances in automated formal methods, certain problems
in system verification and synthesis remain challenging. Examples include the
verification of quantitative properties of software involving constraints on
timing and energy consumption, and the automatic synthesis of systems from
specifications. The major challenges include environment modeling,
incompleteness in specifications, and the complexity of underlying decision
problems.
This position paper proposes sciduction, an approach to tackle these
challenges by integrating inductive inference, deductive reasoning, and
structure hypotheses. Deductive reasoning, which leads from general rules or
concepts to conclusions about specific problem instances, includes techniques
such as logical inference and constraint solving. Inductive inference, which
generalizes from specific instances to yield a concept, includes algorithmic
learning from examples. Structure hypotheses are used to define the class of
artifacts, such as invariants or program fragments, generated during
verification or synthesis. Sciduction constrains inductive and deductive
reasoning using structure hypotheses, and actively combines inductive and
deductive reasoning: for instance, deductive techniques generate examples for
learning, and inductive reasoning is used to guide the deductive engines.
We illustrate this approach with three applications: (i) timing analysis of
software; (ii) synthesis of loop-free programs, and (iii) controller synthesis
for hybrid systems. Some future applications are also discussed
- …