Fiat

Delaware, Benjamin; Pit-Claudel, Clément; Gross, Jason; Chlipala, Adam

doi:10.1145/2676726.2677006

Cited by 56 publications

(18 citation statements)

References 18 publications

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“…Cohen et al (2013) developed a framework for Coq called CoqEAL which automates key steps of data refinement. Delaware et al (2015) presented Fiat, a refinement framework for deductive synthesis of abstract data types in Coq.…”

Section: Refinement Of Programs Data and Proofsmentioning

confidence: 99%

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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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Section: Refinement Of Programs Data and Proofsmentioning

confidence: 99%

“…Other notable frameworks for language design include the Fiat Delaware et al, 2015) framework for Coq, as well as the Hybrid (Felty and Momigliano, 2012) framework for Isabelle/HOL and Coq, which addresses the difficulties of using HOAS with inductive and coinductive proofs.…”

Section: Framework For Language Design and Metatheorymentioning

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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show abstract

“…To integrate this type into Narcissus, the user also supplies the format specification for this type, corresponding encoders and decoders and proofs of their correctness, and a set of tactics explaining how to integrate these pieces into a derivation. Section 5 provides the complete details on these ingredients, but for now we note that the format-_reading specification is nothing more exotic than a nondeterministic function in the style of the Fiat framework [Delaware et al 2015], and that the two lemmas are normal interactive Coq proofs. Wrapping up.…”

Section: 3: Decodermentioning

confidence: 99%

“…In addition to the standard return and bind ( _ ← _; _) operators, this monad includes a set-comprehension operator { x | P x }, which specifies a set via a defining property P on possible return values. The three operators have straightforward interpretations as sets [Delaware et al 2015]:…”

Section: Narcissus Formallymentioning

confidence: 99%

Narcissus: correct-by-construction derivation of decoders and encoders from binary formats

Delaware

Suriyakarn

Pit-Claudel

et al. 2019

Proc. ACM Program. Lang.

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It is a neat result from functional programming that libraries of parser combinators can support rapid construction of decoders for quite a range of formats. With a little more work, the same combinator program can denote both a decoder and an encoder. Unfortunately, the real world is full of gnarly formats, as with the packet formats that make up the standard Internet protocol stack. Most past parser-combinator approaches cannot handle these formats, and the few exceptions require redundancy ś one part of the natural grammar needs to be hand-translated into hints in multiple parts of a parser program. We show how to recover very natural and nonredundant format specifications, covering all popular network packet formats and generating both decoders and encoders automatically. The catch is that we use the Coq proof assistant to derive both kinds of artifacts using tactics, automatically, in a way that guarantees that they form inverses of each other. We used our approach to reimplement packet processing for a full Internet protocol stack, inserting our replacement into the OCaml-based MirageOS unikernel, resulting in minimal performance degradation. ] that aim to reduce opportunities for user error in writing encoders and decoders, but these systems are quite tricky to get right and have themselves been sources of serious security bugs [CVE 2016].Combinator libraries are an alternative approach to the rapid development of parsers which has proven particularly popular in the functional-programming community [Leijen and Meijer 2001]. This approach has been adapted to generate both parsers and pretty printers from single programs [Kennedy 2004;Rendel and Ostermann 2010]. Unfortunately, unverified combinator libraries suffer from the same potential for bugs as code-generation frameworks, with the additional possibility for users to introduce errors when extending the library with new combinators. This paper presents Narcissus, a combinator-style framework for the Coq proof assistant that eliminates the possibility of such bugs, enabling the derivation of encoders and decoders that are correct by construction. Each derived encoder and decoder is backed by a machine-checked functionalcorrectness proof, and Narcissus leverages Coq's proof automation to help automate both the construction of encoders and decoders and their correctness proofs. Key to our approach is how it propagates information through a derivation, in order to generate decoders and encoders for the sorts of non-context-free languages that often appear in standard networking protocols.We begin by introducing the key features of Narcissus with a series of increasingly complex examples, leading to a hypothetical format of packets sent by a temperature sensor to a smart home controller. In order to build up the reader's intuition, we deliberately delay a discussion of the full details of our approach until Section 2. The code accompanying our tour is included in the Narcissus repository 1 in the src/Narcissus/Examples/README.v file, and can be run in Coq ver...

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“…This class of techniques has proven to be successful, for example, in automating the development of concurrent data-structures [77] or signal processing pipelines [60]. The growing power of interactive theorem provers such as Coq have also made it possible to get strong correctness guarantees from code developed through this approach [22]. The main drawback of this class of techniques is that while they tend not to suffer from the same scalability problems as the search-based techniquesbecause they break the problem into a number of small local reasoning steps-they tend to be domain specific, because they rely on carefully engineered deductive rules for the particular problem domain to operate effectively.…”

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

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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.

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Fiat

Cited by 56 publications

References 18 publications

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

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

Narcissus: correct-by-construction derivation of decoders and encoders from binary formats

The three pillars of machine programming

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