Deriving Invariants by Algorithmic Learning, Decision Procedures, and Predicate Abstraction

Jung, Young-Seok; Kong, Soonho; Wang, Bow-Yaw; Yi, Kwangkeun

doi:10.1007/978-3-642-11319-2_15

Cited by 17 publications

(45 citation statements)

References 24 publications

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“…Algorithmic learning [36,34] approaches also iteratively invoke search and validate phases. They use a CDNF learning algorithm that requires membership queries, "is a conjunction of atomic predicates contained in the invariant?…”

Section: Related Workmentioning

confidence: 99%

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From Invariant Checking to Invariant Inference Using Randomized Search

Sharma

Aiken

2014

Computer Aided Verification

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Abstract. We describe a general framework c2i for generating an invariant inference procedure from an invariant checking procedure. Given a checker and a language of possible invariants, c2i generates an inference procedure that iteratively invokes two phases. The search phase uses randomized search to discover candidate invariants and the validate phase uses the checker to either prove or refute that the candidate is an actual invariant. To demonstrate the applicability of c2i, we use it to generate inference procedures that prove safety properties of numerical programs, prove non-termination of numerical programs, prove functional specifications of array manipulating programs, prove safety properties of string manipulating programs, and prove functional specifications of heap manipulating programs that use linked list data structures.

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Section: Related Workmentioning

confidence: 99%

“…The use of a decision procedure as a checker for candidate invariants is also not novel [34,36,45,46,42,20,19]. The main contribution of this paper is a general and effective search procedure that makes a framework like c2i feasible.…”

Section: Introductionmentioning

confidence: 99%

From Invariant Checking to Invariant Inference Using Randomized Search

Sharma

Aiken

2014

Computer Aided Verification

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“…Recently, an automated technique based on algorithmic learning and predicate abstraction is proposed [14]. Given a fixed set of atomic predicates and an annotated loop, the learningbased technique can infer a quantifier-free loop invariant generated by the given atomic predicates.…”

Section: Introductionmentioning

confidence: 99%

“…We instead interpolate the loop body with purported loop invariants from the learning algorithm. Our technique can improve the effectiveness and efficiency of the learning-based loop invariant inference algorithm in [14]. Constructing sets of atomic predicates can be fully automatic and on-demand.…”

Section: Introductionmentioning

confidence: 99%

“…Motivated by the interpolation-based abstraction refinement technique, we apply the interpolation theorem to synthesize predicates implicitly implied by program texts. Our technique is able to improve the effectiveness and efficiency of the learning-based loop invariant inference algorithm in [14]. Experiments excerpted from Linux, SPEC2000, and Tar source codes are reported.…”

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confidence: 99%

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Predicate Generation for Learning-Based Quantifier-Free Loop Invariant Inference

Jung

Lee

Wang

et al. 2011

Tools and Algorithms for the Construction and Analysis of Systems

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Abstract. We address the predicate generation problem in the context of loop invariant inference. Motivated by the interpolation-based abstraction refinement technique, we apply the interpolation theorem to synthesize predicates implicitly implied by program texts. Our technique is able to improve the effectiveness and efficiency of the learning-based loop invariant inference algorithm in [14]. Experiments excerpted from Linux, SPEC2000, and Tar source codes are reported.

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