Combinatorial sketching for finite programs

Solar-Lezama, Armando; Tancau, Liviu; Bodík, Rastislav; Seshia, Sanjit A.; Saraswat, Vijay

doi:10.1145/1168857.1168907

Cited by 412 publications

(116 citation statements)

References 12 publications

Supporting

Mentioning

116

Contrasting

Order By: Relevance

“…Solving Horn clauses with uninterpreted functions is closely connected to sketching-based program synthesis problems [Solar-Lezama et al 2006], where we want to find substitutions for holes in a program to ensure that the whole program satisfies a target property. One could reformulate our problem of solving Horn clauses with uninterpreted functions as solving a program synthesis problem, and, e.g., use the template-based technique of Srivastava et al [2010] to find an inductive invariant and a coupling strategy.…”

Section: Related Workmentioning

confidence: 99%

“…One could reformulate our problem of solving Horn clauses with uninterpreted functions as solving a program synthesis problem, and, e.g., use the template-based technique of Srivastava et al [2010] to find an inductive invariant and a coupling strategy. In our implementation, we used a synthesizeand-verify loop, a methodology that has appeared in different guises, for example, in the Sketch synthesizer [Solar-Lezama et al 2006] and the e-hsf Horn-clause solver [Beyene et al 2013].…”

Section: Related Workmentioning

confidence: 99%

“…We employ established techniques from synthesis and program verification to solve these simplified constraints. Specifically, we use counterexample-guided inductive synthesis (cegis) [Solar-Lezama et al 2006] to discover a strategy f , and predicate abstraction [Graf and Saïdi 1997] to prove that f is a winning strategy. Implementation and Evaluation.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Synthesizing coupling proofs of differential privacy

Albarghouthi

Hsu

2017

Proc. ACM Program. Lang.

100

View full text Add to dashboard Cite

Differential privacy has emerged as a promising probabilistic formulation of privacy, generating intense interest within academia and industry. We present a push-button, automated technique for verifying ε-differential privacy of sophisticated randomized algorithms. We make several conceptual, algorithmic, and practical contributions: (i) Inspired by the recent advances on approximate couplings and randomness alignment, we present a new proof technique called coupling strategies, which casts differential privacy proofs as a winning strategy in a game where we have finite privacy resources to expend. (ii) To discover a winning strategy, we present a constraint-based formulation of the problem as a set of Horn modulo couplings (hmc) constraints, a novel combination of first-order Horn clauses and probabilistic constraints. (iii) We present a technique for solving hmc constraints by transforming probabilistic constraints into logical constraints with uninterpreted functions. (iv) Finally, we implement our technique in the FairSquare verifier and provide the first automated privacy proofs for a number of challenging algorithms from the differential privacy literature, including Report Noisy Max, the Exponential Mechanism, and the Sparse Vector Mechanism.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Synthesizing coupling proofs of differential privacy

Albarghouthi

Hsu

2017

Proc. ACM Program. Lang.

100

View full text Add to dashboard Cite

show abstract

“…These techniques achieve efficiency by ruling out large sets of possible programs without exploring them one-by-one, usually by discovering that particular sub-structures can never be part of a correct solution [3,28,52,57,75]. The second class involves symbolic search techniques, where the entire program space is represented symbolically, either using a special purpose representation [32,58], or, in the case of constraint-based synthesis, by reducing it to a set of constraints whose solution can be mapped to a concrete program, which can be solved using a SAT or SMT solver [33,72,74], or in some cases a numerical optimization procedure [17].…”

Section: Program Synthesismentioning

confidence: 99%

The three pillars of machine programming

Gottschlich

Solar-Lezama

Tatbul

et al. 2018

Proceedings of the 2nd ACM SIGPLAN International Workshop on Machine Learning and Programming Languages

Self Cite

View full text Add to dashboard Cite

In this position paper, we describe our vision of the future of machine programming through a categorical examination of three pillars of research. Those pillars are: (i) intention, (ii) invention, and (iii) adaptation. Intention emphasizes advancements in the human-to-computer and computer-to-machinelearning interfaces. Invention emphasizes the creation or refinement of algorithms or core hardware and software building blocks through machine learning (ML). Adaptation emphasizes advances in the use of ML-based constructs to autonomously evolve software.

show abstract

“…This demonstrates that the convergence rate of learning in Counterexample-Guided Inductive Synthesis [e.g. Jha et al 2010;Jha and Seshia 2017;Solar-Lezama et al 2006] depends on the form of examples. We also establish impossibility results for directly applying algorithms from concept learning to invariant inference.…”

Section: Introductionmentioning

confidence: 99%

Complexity and information in invariant inference

Feldman

Immerman

Sagiv

et al. 2019

Proc. ACM Program. Lang.

View full text Add to dashboard Cite

This paper addresses the complexity of SAT-based invariant inference, a prominent approach to safety verification. We consider the problem of inferring an inductive invariant of polynomial length given a transition system and a safety property. We analyze the complexity of this problem in a black-box model, called the Hoare-query model, which is general enough to capture algorithms such as IC3/PDR and its variants. An algorithm in this model learns about the system's reachable states by querying the validity of Hoare triples.We show that in general an algorithm in the Hoare-query model requires an exponential number of queries. Our lower bound is information-theoretic and applies even to computationally unrestricted algorithms, showing that no choice of generalization from the partial information obtained in a polynomial number of Hoare queries can lead to an efficient invariant inference procedure in this class.We then show, for the first time, that by utilizing rich Hoare queries, as done in PDR, inference can be exponentially more efficient than approaches such as ICE learning, which only utilize inductiveness checks of candidates. We do so by constructing a class of transition systems for which a simple version of PDR with a single frame infers invariants in a polynomial number of queries, whereas every algorithm using only inductiveness checks and counterexamples requires an exponential number of queries.Our results also shed light on connections and differences with the classical theory of exact concept learning with queries, and imply that learning from counterexamples to induction is harder than classical exact learning from labeled examples. This demonstrates that the convergence rate of Counterexample-Guided Inductive Synthesis depends on the form of counterexamples.

show abstract

Combinatorial sketching for finite programs

Cited by 412 publications

References 12 publications

Synthesizing coupling proofs of differential privacy

Synthesizing coupling proofs of differential privacy

The three pillars of machine programming

Complexity and information in invariant inference

Contact Info

Product

Resources

About