Synthesis from hyperproperties

Finkbeiner, Bernd; Hahn, Christopher; Lukert, Philip; Stenger, Marvin; Tentrup, Leander

doi:10.1007/s00236-019-00358-2

Cited by 23 publications

(26 citation statements)

References 43 publications

(86 reference statements)

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“…The hierarchy of hyperlogics beyond HyperLTL were studied in [11]. The synthesis problem for HyperLTL has been studied in [3] in the form of program repair, in [4] in the form of controller synthesis, and in [18] for the general case.…”

Section: Related Workmentioning

confidence: 99%

Bounded Model Checking for Hyperproperties

Hsu

Sánchez

Bonakdarpour

2021

Tools and Algorithms for the Construction and Analysis of Systems

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This paper introduces a bounded model checking (BMC) algorithm for hyperproperties expressed in HyperLTL, which — to the best of our knowledge — is the first such algorithm. Just as the classic BMC technique for LTL primarily aims at finding bugs, our approach also targets identifying counterexamples. BMC for LTL is reduced to SAT solving, because LTL describes a property via inspecting individual traces. Our BMC approach naturally reduces to QBF solving, as HyperLTL allows explicit and simultaneous quantification over multiple traces. We report on successful and efficient model checking, implemented in our tool called , of a rich set of experiments on a variety of case studies, including security, concurrent data structures, path planning for robots, and mutation testing.

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Section: Related Workmentioning

confidence: 99%

Bounded Model Checking for Hyperproperties

Hsu

Sánchez

Bonakdarpour

2021

Tools and Algorithms for the Construction and Analysis of Systems

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“…With theoretical solutions, already in 1969 by Büchi and Landweber [7], the field has matured today. From a foundational point of view, advances have been made algorithmically, e.g., with a quasi-polynomial algorithm for parity games [9], conceptually with distributed [40] and bounded synthesis [16], or expressiveness-wise, e.g., GR(1) [39] synthesis, which is an efficient fragment of LTL or synthesis for security properties [17].…”

Section: Related Workmentioning

confidence: 99%

Neural Circuit Synthesis from Specification Patterns

Schmitt,

Hahn,

Rabe

et al. 2021

Preprint

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We train hierarchical Transformers on the task of synthesizing hardware circuits directly out of high-level logical specifications in linear-time temporal logic (LTL). The LTL synthesis problem is a well-known algorithmic challenge with a long history and an annual competition is organized to track the improvement of algorithms and tooling over time. New approaches using machine learning might open a lot of possibilities in this area, but suffer from the lack of sufficient amounts of training data. In this paper, we consider a method to generate large amounts of additional training data, i.e., pairs of specifications and circuits implementing them. We ensure that this synthetic data is sufficiently close to human-written specifications by mining common patterns from the specifications used in the synthesis competitions. We show that hierarchical Transformers trained on this synthetic data solve a significant portion of problems from the synthesis competitions, and even out-of-distribution examples from a recent case study.Preprint. Under review.

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“…Hyperproperties of interest include security properties like non-interference or observational determinism since it can only be inferred from combinations of traces and their relation to each other whether a system fulfills these properties. Analysis methods for hyperproperties have been proposed in many contexts, including abstract interpretation Pasqua 2017, 2018], runtime verification , synthesis [Finkbeiner et al 2020] and model checking [Clarkson et al 2014;Finkbeiner et al 2015;Gutsfeld et al 2020a;Rabe 2016]. In model checking, several temporal logics for hyperproperties have been proposed, including hyperized variants of LTL [Clarkson et al 2014;Finkbeiner et al 2015;Rabe 2016], CTL * [Clarkson et al 2014;Finkbeiner et al 2015;Rabe 2016], QPTL [Coenen et al 2019;Rabe 2016] and PDL−Δ [Gutsfeld et al 2020a].…”

Section: Introductionmentioning

confidence: 99%

Automata and fixpoints for asynchronous hyperproperties

Gutsfeld

Müller-Olm

Ohrem

2021

Proc. ACM Program. Lang.

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Hyperproperties have received increasing attention in the last decade due to their importance e.g. for security analyses. Past approaches have focussed on synchronous analyses, i.e. techniques in which different paths are compared lockstepwise. In this paper, we systematically study asynchronous analyses for hyperproperties by introducing both a novel automata model (Alternating Asynchronous Parity Automata) and the temporal fixpoint calculus H µ , the first fixpoint calculus that can systematically express hyperproperties in an asynchronous manner and at the same time subsumes the existing logic HyperLTL. We show that the expressive power of both models coincides over fixed path assignments. The high expressive power of both models is evidenced by the fact that decision problems of interest are highly undecidable, i.e. not even arithmetical. As a remedy, we propose approximative analyses for both models that also induce natural decidable fragments.

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Synthesis from hyperproperties

Cited by 23 publications

References 43 publications

Bounded Model Checking for Hyperproperties

Bounded Model Checking for Hyperproperties

Neural Circuit Synthesis from Specification Patterns

Automata and fixpoints for asynchronous hyperproperties

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