A Learning-Based Fact Selector for Isabelle/HOL

Blanchette, Jasmin Christian; Greenaway, David; Kaliszyk, Cezary; Kühlwein, Daniel; Urban, Josef

doi:10.1007/s10817-016-9362-8

Cited by 49 publications

(65 citation statements)

References 45 publications

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“…Augmentation of hammer components to increase effectiveness and success rates is an active research topic (Blanchette et al, 2016a;Wang et al, 2017;Peng and Ma, 2017).…”

Section: General-purpose Automationmentioning

confidence: 99%

QED at Large: A Survey of Engineering of Formally Verified Software

Ringer

Palmskog

Sergey

et al. 2019

FNT in Programming Languages

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Development of formal proofs of correctness of programs can increase actual and perceived reliability and facilitate better understanding of program specifications and their underlying assumptions. Tools supporting such development have been available for over 40 years, but have only recently seen wide practical use. Projects based on construction of machine-checked formal proofs are now reaching an unprecedented scale, comparable to large software projects, which leads to new challenges in proof development and maintenance. Despite its increasing importance, the field of proof engineering is seldom considered in its own right; related theories, techniques, and tools span many fields and venues. This survey of the literature presents a holistic understanding of proof engineering for program correctness, covering impact in practice, foundations, proof automation, proof organization, and practical proof development.

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“…Augmentation of hammer components to increase effectiveness and success rates is an active research topic (Blanchette et al, 2016a;Wang et al, 2017;Peng and Ma, 2017).…”

Section: General-purpose Automationmentioning

confidence: 99%

QED at Large: A Survey of Engineering of Formally Verified Software

Ringer

Palmskog

Sergey

et al. 2019

FNT in Programming Languages

View full text Add to dashboard Cite

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“…Where4's interaction with Why3 is inspired by the use of machine learning in the Sledgehammer tool [10] which allows the use of SMT solvers in the interactive theorem prover Isabelle/HOL. We aspired to Sledgehammer's 'zero click, zero maintenance, zero overhead' philosophy in this regard: it should not interfere with a Why3 user's normal work-flow nor should it penalise those who do not use it.…”

Section: Implementing Wherein Ocamlmentioning

confidence: 99%

Predicting SMT Solver Performance for Software Verification

Healy

Monahan

Power

2017

Electron. Proc. Theor. Comput. Sci.

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The Why3 IDE and verification system facilitates the use of a wide range of Satisfiability Modulo Theories (SMT) solvers through a driver-based architecture. We present Where4: a portfolio-based approach to discharge Why3 proof obligations. We use data analysis and machine learning techniques on static metrics derived from program source code. Our approach benefits software engineers by providing a single utility to delegate proof obligations to the solvers most likely to return a useful result. It does this in a time-efficient way using existing Why3 and solver installations - without requiring low-level knowledge about SMT solver operation from the user.Comment: In Proceedings F-IDE 2016, arXiv:1701.0792

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“…On the other hand, with small exceptions, ATPs are still significantly weaker than trained mathematicians in finding proofs in most research domains.Recently, machine learning over large formal corpora created from ITP libraries [37,28,19] has started to be used to develop guidance of ATP systems [39,25,2]. This has already produced strong systems for selecting relevant facts for proving new conjectures over large formal libraries [1,4,9]. More recently, machine learning has also started to be used to guide the internal search of the ATP systems.…”

mentioning

confidence: 99%

ENIGMA-NG: Efficient Neural and Gradient-Boosted Inference Guidance for E

Chvalovský

Jakubův

Suda

et al. 2019

Lecture Notes in Computer Science

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We describe an efficient implementation of clause guidance in saturation-based automated theorem provers extending the ENIGMA approach. Unlike in the first ENIGMA implementation where fast linear classifier is trained and used together with manually engineered features, we have started to experiment with more sophisticated state-of-the-art machine learning methods such as gradient boosted trees and recursive neural networks. In particular the latter approach poses challenges in terms of efficiency of clause evaluation, however, we show that deep integration of the neural evaluation with the ATP data-structures can largely amortize this cost and lead to competitive real-time results. Both methods are evaluated on a large dataset of theorem proving problems and compared with the previous approaches. The resulting methods improve on the manually designed clause guidance, providing the first practically convincing application of gradient-boosted and neural clause guidance in saturation-style automated theorem provers. IntroductionAutomated theorem provers (ATPs) [32] have been developed for decades by manually designing proof calculi and search heuristics. Their power has been growing and they are already very useful, e.g., as parts of large interactive theorem proving (ITP) verification toolchains (hammers) [5]. On the other hand, with small exceptions, ATPs are still significantly weaker than trained mathematicians in finding proofs in most research domains.Recently, machine learning over large formal corpora created from ITP libraries [37,28,19] has started to be used to develop guidance of ATP systems [39,25,2]. This has already produced strong systems for selecting relevant facts for proving new conjectures over large formal libraries [1,4,9]. More recently, machine learning has also started to be used to guide the internal search of the ATP systems. In sophisticated saturation-style provers this has been done by feedback loops for strategy invention [38,16,33] and by using supervised learning [14,26] to select the next given clause [27]. In the simpler connection tableau systems such as leancop [29], supervised learning has been used to choose ⋆ Supported by the ERC Consolidator grant no. 649043 AI4REASON, and by the Czech project AI&Reasoning CZ.02.1.01/0.0/0.0/15 003/0000466 and the European Regional Development Fund.

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A Learning-Based Fact Selector for Isabelle/HOL

Cited by 49 publications

References 45 publications

QED at Large: A Survey of Engineering of Formally Verified Software

QED at Large: A Survey of Engineering of Formally Verified Software

Predicting SMT Solver Performance for Software Verification

ENIGMA-NG: Efficient Neural and Gradient-Boosted Inference Guidance for E

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