4,380 research outputs found
Proof-Pattern Recognition and Lemma Discovery in ACL2
We present a novel technique for combining statistical machine learning for
proof-pattern recognition with symbolic methods for lemma discovery. The
resulting tool, ACL2(ml), gathers proof statistics and uses statistical
pattern-recognition to pre-processes data from libraries, and then suggests
auxiliary lemmas in new proofs by analogy with already seen examples. This
paper presents the implementation of ACL2(ml) alongside theoretical
descriptions of the proof-pattern recognition and lemma discovery methods
involved in it
Invariant Synthesis for Incomplete Verification Engines
We propose a framework for synthesizing inductive invariants for incomplete
verification engines, which soundly reduce logical problems in undecidable
theories to decidable theories. Our framework is based on the counter-example
guided inductive synthesis principle (CEGIS) and allows verification engines to
communicate non-provability information to guide invariant synthesis. We show
precisely how the verification engine can compute such non-provability
information and how to build effective learning algorithms when invariants are
expressed as Boolean combinations of a fixed set of predicates. Moreover, we
evaluate our framework in two verification settings, one in which verification
engines need to handle quantified formulas and one in which verification
engines have to reason about heap properties expressed in an expressive but
undecidable separation logic. Our experiments show that our invariant synthesis
framework based on non-provability information can both effectively synthesize
inductive invariants and adequately strengthen contracts across a large suite
of programs
Synthesis of sup-interpretations: a survey
In this paper, we survey the complexity of distinct methods that allow the
programmer to synthesize a sup-interpretation, a function providing an upper-
bound on the size of the output values computed by a program. It consists in a
static space analysis tool without consideration of the time consumption.
Although clearly related, sup-interpretation is independent from termination
since it only provides an upper bound on the terminating computations. First,
we study some undecidable properties of sup-interpretations from a theoretical
point of view. Next, we fix term rewriting systems as our computational model
and we show that a sup-interpretation can be obtained through the use of a
well-known termination technique, the polynomial interpretations. The drawback
is that such a method only applies to total functions (strongly normalizing
programs). To overcome this problem we also study sup-interpretations through
the notion of quasi-interpretation. Quasi-interpretations also suffer from a
drawback that lies in the subterm property. This property drastically restricts
the shape of the considered functions. Again we overcome this problem by
introducing a new notion of interpretations mainly based on the dependency
pairs method. We study the decidability and complexity of the
sup-interpretation synthesis problem for all these three tools over sets of
polynomials. Finally, we take benefit of some previous works on termination and
runtime complexity to infer sup-interpretations.Comment: (2012
Complexity Bounds for Ordinal-Based Termination
`What more than its truth do we know if we have a proof of a theorem in a
given formal system?' We examine Kreisel's question in the particular context
of program termination proofs, with an eye to deriving complexity bounds on
program running times.
Our main tool for this are length function theorems, which provide complexity
bounds on the use of well quasi orders. We illustrate how to prove such
theorems in the simple yet until now untreated case of ordinals. We show how to
apply this new theorem to derive complexity bounds on programs when they are
proven to terminate thanks to a ranking function into some ordinal.Comment: Invited talk at the 8th International Workshop on Reachability
Problems (RP 2014, 22-24 September 2014, Oxford
ACL2(ml):machine-learning for ACL2
ACL2(ml) is an extension for the Emacs interface of ACL2. This tool uses
machine-learning to help the ACL2 user during the proof-development. Namely,
ACL2(ml) gives hints to the user in the form of families of similar theorems,
and generates auxiliary lemmas automatically. In this paper, we present the two
most recent extensions for ACL2(ml). First, ACL2(ml) can suggest now families
of similar function definitions, in addition to the families of similar
theorems. Second, the lemma generation tool implemented in ACL2(ml) has been
improved with a method to generate preconditions using the guard mechanism of
ACL2. The user of ACL2(ml) can also invoke directly the latter extension to
obtain preconditions for his own conjectures.Comment: In Proceedings ACL2 2014, arXiv:1406.123
An Instantiation-Based Approach for Solving Quantified Linear Arithmetic
This paper presents a framework to derive instantiation-based decision
procedures for satisfiability of quantified formulas in first-order theories,
including its correctness, implementation, and evaluation. Using this framework
we derive decision procedures for linear real arithmetic (LRA) and linear
integer arithmetic (LIA) formulas with one quantifier alternation. Our
procedure can be integrated into the solving architecture used by typical SMT
solvers. Experimental results on standardized benchmarks from model checking,
static analysis, and synthesis show that our implementation of the procedure in
the SMT solver CVC4 outperforms existing tools for quantified linear
arithmetic
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