Inside Coq's Type System
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Experience Implementing a Performant Category-Theory Library in Coq
Experience Implementing a Performant Category-Theory Library in Coq Jason Gross, Adam Chlipala, and David I. Spivak Massachusetts Institute of Technology, Cambridge, MA, USA [email protected], [email protected], [email protected] Abstract. We describe our experience implementing a broad category- theory library in Coq. Category theory and computational performance are not usually mentioned in the same breath, but we have needed sub- stantial engineering effort to teach Coq to cope with large categorical constructions without slowing proof script processing unacceptably. In this paper, we share the lessons we have learned about how to repre- sent very abstract mathematical objects and arguments in Coq and how future proof assistants might be designed to better support such rea- soning. One particular encoding trick to which we draw attention al- lows category-theoretic arguments involving duality to be internalized in Coq's logic with definitional equality. Ours may be the largest Coq development to date that uses the relatively new Coq version developed by homotopy type theorists, and we reflect on which new features were especially helpful. Keywords: Coq · category theory · homotopy type theory · duality · performance 1 Introduction Category theory [36] is a popular all-encompassing mathematical formalism that casts familiar mathematical ideas from many domains in terms of a few unifying concepts. A category can be described as a directed graph plus algebraic laws stating equivalences between paths through the graph. Because of this spar- tan philosophical grounding, category theory is sometimes referred to in good humor as \formal abstract nonsense." Certainly the popular perception of cat- egory theory is quite far from pragmatic issues of implementation. -
A Polymorphic Type System for Extensible Records and Variants
A Polymorphic Typ e System for Extensible Records and Variants Benedict R. Gaster and Mark P. Jones Technical rep ort NOTTCS-TR-96-3, November 1996 Department of Computer Science, University of Nottingham, University Park, Nottingham NG7 2RD, England. fbrg,[email protected] Abstract b oard, and another indicating a mouse click at a par- ticular p oint on the screen: Records and variants provide exible ways to construct Event = Char + Point : datatyp es, but the restrictions imp osed by practical typ e systems can prevent them from b eing used in ex- These de nitions are adequate, but they are not par- ible ways. These limitations are often the result of con- ticularly easy to work with in practice. For example, it cerns ab out eciency or typ e inference, or of the di- is easy to confuse datatyp e comp onents when they are culty in providing accurate typ es for key op erations. accessed by their p osition within a pro duct or sum, and This pap er describ es a new typ e system that reme- programs written in this way can b e hard to maintain. dies these problems: it supp orts extensible records and To avoid these problems, many programming lan- variants, with a full complement of p olymorphic op era- guages allow the comp onents of pro ducts, and the al- tions on each; and it o ers an e ective type inference al- ternatives of sums, to b e identi ed using names drawn gorithm and a simple compilation metho d. -
Testing Noninterference, Quickly
Testing Noninterference, Quickly Cat˘ alin˘ Hrit¸cu1 John Hughes2 Benjamin C. Pierce1 Antal Spector-Zabusky1 Dimitrios Vytiniotis3 Arthur Azevedo de Amorim1 Leonidas Lampropoulos1 1University of Pennsylvania 2Chalmers University 3Microsoft Research Abstract To answer this question, we take as a case study the task of Information-flow control mechanisms are difficult to design and extending a simple abstract stack-and-pointer machine to track dy- labor intensive to prove correct. To reduce the time wasted on namic information flow and enforce termination-insensitive nonin- proof attempts doomed to fail due to broken definitions, we ad- terference [23]. Although our machine is simple, this exercise is vocate modern random testing techniques for finding counterexam- both nontrivial and novel. While simpler notions of dynamic taint ples during the design process. We show how to use QuickCheck, tracking are well studied for both high- and low-level languages, a property-based random-testing tool, to guide the design of a sim- it has only recently been shown [1, 24] that dynamic checks are ple information-flow abstract machine. We find that both sophis- capable of soundly enforcing strong security properties. Moreover, ticated strategies for generating well-distributed random programs sound dynamic IFC has been studied only in the context of lambda- and readily falsifiable formulations of noninterference properties calculi [1, 16, 25] and While programs [24]; the unstructured con- are critically important. We propose several approaches and eval- trol flow of a low-level machine poses additional challenges. (Test- uate their effectiveness on a collection of injected bugs of varying ing of static IFC mechanisms is left as future work.) subtlety. -
An Anti-Locally-Nameless Approach to Formalizing Quantifiers Olivier Laurent
An Anti-Locally-Nameless Approach to Formalizing Quantifiers Olivier Laurent To cite this version: Olivier Laurent. An Anti-Locally-Nameless Approach to Formalizing Quantifiers. Certified Programs and Proofs, Jan 2021, virtual, Denmark. pp.300-312, 10.1145/3437992.3439926. hal-03096253 HAL Id: hal-03096253 https://hal.archives-ouvertes.fr/hal-03096253 Submitted on 4 Jan 2021 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. An Anti-Locally-Nameless Approach to Formalizing Quantifiers Olivier Laurent Univ Lyon, EnsL, UCBL, CNRS, LIP LYON, France [email protected] Abstract all cases have been covered. This works perfectly well for We investigate the possibility of formalizing quantifiers in various propositional systems, but as soon as (first-order) proof theory while avoiding, as far as possible, the use of quantifiers enter the picture, we have to face one ofthe true binding structures, α-equivalence or variable renam- nightmares of formalization: quantifiers are binders... The ings. We propose a solution with two kinds of variables in question of the formalization of binders does not yet have an 1 terms and formulas, as originally done by Gentzen. In this answer which makes perfect consensus . -
Generic Programming
Generic Programming July 21, 1998 A Dagstuhl Seminar on the topic of Generic Programming was held April 27– May 1, 1998, with forty seven participants from ten countries. During the meeting there were thirty seven lectures, a panel session, and several problem sessions. The outcomes of the meeting include • A collection of abstracts of the lectures, made publicly available via this booklet and a web site at http://www-ca.informatik.uni-tuebingen.de/dagstuhl/gpdag.html. • Plans for a proceedings volume of papers submitted after the seminar that present (possibly extended) discussions of the topics covered in the lectures, problem sessions, and the panel session. • A list of generic programming projects and open problems, which will be maintained publicly on the World Wide Web at http://www-ca.informatik.uni-tuebingen.de/people/musser/gp/pop/index.html http://www.cs.rpi.edu/˜musser/gp/pop/index.html. 1 Contents 1 Motivation 3 2 Standards Panel 4 3 Lectures 4 3.1 Foundations and Methodology Comparisons ........ 4 Fundamentals of Generic Programming.................. 4 Jim Dehnert and Alex Stepanov Automatic Program Specialization by Partial Evaluation........ 4 Robert Gl¨uck Evaluating Generic Programming in Practice............... 6 Mehdi Jazayeri Polytypic Programming........................... 6 Johan Jeuring Recasting Algorithms As Objects: AnAlternativetoIterators . 7 Murali Sitaraman Using Genericity to Improve OO Designs................. 8 Karsten Weihe Inheritance, Genericity, and Class Hierarchies.............. 8 Wolf Zimmermann 3.2 Programming Methodology ................... 9 Hierarchical Iterators and Algorithms................... 9 Matt Austern Generic Programming in C++: Matrix Case Study........... 9 Krzysztof Czarnecki Generative Programming: Beyond Generic Programming........ 10 Ulrich Eisenecker Generic Programming Using Adaptive and Aspect-Oriented Programming . -
Data Types and Variables
Color profile: Generic CMYK printer profile Composite Default screen Complete Reference / Visual Basic 2005: The Complete Reference / Petrusha / 226033-5 / Chapter 2 2 Data Types and Variables his chapter will begin by examining the intrinsic data types supported by Visual Basic and relating them to their corresponding types available in the .NET Framework’s Common TType System. It will then examine the ways in which variables are declared in Visual Basic and discuss variable scope, visibility, and lifetime. The chapter will conclude with a discussion of boxing and unboxing (that is, of converting between value types and reference types). Visual Basic Data Types At the center of any development or runtime environment, including Visual Basic and the .NET Common Language Runtime (CLR), is a type system. An individual type consists of the following two items: • A set of values. • A set of rules to convert every value not in the type into a value in the type. (For these rules, see Appendix F.) Of course, every value of another type cannot always be converted to a value of the type; one of the more common rules in this case is to throw an InvalidCastException, indicating that conversion is not possible. Scalar or primitive types are types that contain a single value. Visual Basic 2005 supports two basic kinds of scalar or primitive data types: structured data types and reference data types. All data types are inherited from either of two basic types in the .NET Framework Class Library. Reference types are derived from System.Object. Structured data types are derived from the System.ValueType class, which in turn is derived from the System.Object class. -
Mathematics in the Computer
Mathematics in the Computer Mario Carneiro Carnegie Mellon University April 26, 2021 1 / 31 Who am I? I PhD student in Logic at CMU I Proof engineering since 2013 I Metamath (maintainer) I Lean 3 (maintainer) I Dabbled in Isabelle, HOL Light, Coq, Mizar I Metamath Zero (author) Github: digama0 I Proved 37 of Freek’s 100 theorems list in Zulip: Mario Carneiro Metamath I Lots of library code in set.mm and mathlib I Say hi at https://leanprover.zulipchat.com 2 / 31 I Found Metamath via a random internet search I they already formalized half of the book! I .! . and there is some stuff on cofinality they don’t have yet, maybe I can help I Got involved, did it as a hobby for a few years I Got a job as an android developer, kept on the hobby I Norm Megill suggested that I submit to a (Mizar) conference, it went well I Met Leo de Moura (Lean author) at a conference, he got me in touch with Jeremy Avigad (my current advisor) I Now I’m a PhD at CMU philosophy! How I got involved in formalization I Undergraduate at Ohio State University I Math, CS, Physics I Reading Takeuti & Zaring, Axiomatic Set Theory 3 / 31 I they already formalized half of the book! I .! . and there is some stuff on cofinality they don’t have yet, maybe I can help I Got involved, did it as a hobby for a few years I Got a job as an android developer, kept on the hobby I Norm Megill suggested that I submit to a (Mizar) conference, it went well I Met Leo de Moura (Lean author) at a conference, he got me in touch with Jeremy Avigad (my current advisor) I Now I’m a PhD at CMU philosophy! How I got involved in formalization I Undergraduate at Ohio State University I Math, CS, Physics I Reading Takeuti & Zaring, Axiomatic Set Theory I Found Metamath via a random internet search 3 / 31 I . -
Static Typing Where Possible, Dynamic Typing When Needed: the End of the Cold War Between Programming Languages
Intended for submission to the Revival of Dynamic Languages Static Typing Where Possible, Dynamic Typing When Needed: The End of the Cold War Between Programming Languages Erik Meijer and Peter Drayton Microsoft Corporation Abstract Advocates of dynamically typed languages argue that static typing is too rigid, and that the softness of dynamically lan- This paper argues that we should seek the golden middle way guages makes them ideally suited for prototyping systems with between dynamically and statically typed languages. changing or unknown requirements, or that interact with other systems that change unpredictably (data and application in- tegration). Of course, dynamically typed languages are indis- 1 Introduction pensable for dealing with truly dynamic program behavior such as method interception, dynamic loading, mobile code, runtime <NOTE to="reader"> Please note that this paper is still very reflection, etc. much work in progress and as such the presentation is unpol- In the mother of all papers on scripting [16], John Ousterhout ished and possibly incoherent. Obviously many citations to argues that statically typed systems programming languages related and relevant work are missing. We did however do our make code less reusable, more verbose, not more safe, and less best to make it provocative. </NOTE> expressive than dynamically typed scripting languages. This Advocates of static typing argue that the advantages of static argument is parroted literally by many proponents of dynami- typing include earlier detection of programming mistakes (e.g. cally typed scripting languages. We argue that this is a fallacy preventing adding an integer to a boolean), better documenta- and falls into the same category as arguing that the essence of tion in the form of type signatures (e.g. -
Software II: Principles of Programming Languages
Software II: Principles of Programming Languages Lecture 6 – Data Types Some Basic Definitions • A data type defines a collection of data objects and a set of predefined operations on those objects • A descriptor is the collection of the attributes of a variable • An object represents an instance of a user- defined (abstract data) type • One design issue for all data types: What operations are defined and how are they specified? Primitive Data Types • Almost all programming languages provide a set of primitive data types • Primitive data types: Those not defined in terms of other data types • Some primitive data types are merely reflections of the hardware • Others require only a little non-hardware support for their implementation The Integer Data Type • Almost always an exact reflection of the hardware so the mapping is trivial • There may be as many as eight different integer types in a language • Java’s signed integer sizes: byte , short , int , long The Floating Point Data Type • Model real numbers, but only as approximations • Languages for scientific use support at least two floating-point types (e.g., float and double ; sometimes more • Usually exactly like the hardware, but not always • IEEE Floating-Point Standard 754 Complex Data Type • Some languages support a complex type, e.g., C99, Fortran, and Python • Each value consists of two floats, the real part and the imaginary part • Literal form real component – (in Fortran: (7, 3) imaginary – (in Python): (7 + 3j) component The Decimal Data Type • For business applications (money) -
Xmonad in Coq: Programming a Window Manager in a Proof Assistant
xmonad in Coq Programming a window manager in a proof assistant Wouter Swierstra Haskell Symposium 2012 Coq Coq • Coq is ‘just’ a total functional language; • Extraction lets you turn Coq programs into OCaml, Haskell, or Scheme code. • Extraction discards proofs, but may introduce ‘unsafe’ coercions. Demo Extraction to Haskell is not popular. xmonad xmonad • A tiling window manager for X: • arranges windows over the whole screen; • written and configured in Haskell; • has several tens of thousands of users. xmonad: design principles ReaderT StateT IO monad This paper • This paper describes a reimplementation of xmonad’s pure core in Coq; • Extracting Haskell code produces a drop-in replacement for the original module; • Documents my experience. Does it work? Blood Sweat Shell script What happens in the functional core? Data types data Zipper a = Zipper { left :: [a] , focus :: !a , right :: [a] } ... and a lot of functions for zipper manipulation Totality • This project is feasible because most of the functions are structurally recursive. • What about this function? focusLeft (Zipper [] x rs) = let (y : ys) = reverse (x : rs) in Zipper ys y [] Extraction • The basic extracted code is terrible! • uses Peano numbers, extracted Coq booleans, etc. • uses extracted Coq data types for zippers; • generates ‘non-idiomatic’ Haskell. Customizing extraction • There are various hooks to customize the extracted code: • inlining functions; • realizing axioms; • using Haskell data types. Interfacing with Haskell • We would like to use ‘real’ Haskell booleans Extract Inductive bool => "Bool" ["True" "False" ]. • Lots of opportunity to shoot yourself in the foot! Better extracted code • The extracted file uses generated data types and exports ‘too much’ • Solution: • Customize extraction to use hand-coded data types. -
Should Your Specification Language Be Typed?
Should Your Specification Language Be Typed? LESLIE LAMPORT Compaq and LAWRENCE C. PAULSON University of Cambridge Most specification languages have a type system. Type systems are hard to get right, and getting them wrong can lead to inconsistencies. Set theory can serve as the basis for a specification lan- guage without types. This possibility, which has been widely overlooked, offers many advantages. Untyped set theory is simple and is more flexible than any simple typed formalism. Polymorphism, overloading, and subtyping can make a type system more powerful, but at the cost of increased complexity, and such refinements can never attain the flexibility of having no types at all. Typed formalisms have advantages too, stemming from the power of mechanical type checking. While types serve little purpose in hand proofs, they do help with mechanized proofs. In the absence of verification, type checking can catch errors in specifications. It may be possible to have the best of both worlds by adding typing annotations to an untyped specification language. We consider only specification languages, not programming languages. Categories and Subject Descriptors: D.2.1 [Software Engineering]: Requirements/Specifica- tions; D.2.4 [Software Engineering]: Software/Program Verification—formal methods; F.3.1 [Logics and Meanings of Programs]: Specifying and Verifying and Reasoning about Pro- grams—specification techniques General Terms: Verification Additional Key Words and Phrases: Set theory, specification, types Editors’ introduction. We have invited the following -
A Survey of Engineering of Formally Verified Software
The version of record is available at: http://dx.doi.org/10.1561/2500000045 QED at Large: A Survey of Engineering of Formally Verified Software Talia Ringer Karl Palmskog University of Washington University of Texas at Austin [email protected] [email protected] Ilya Sergey Milos Gligoric Yale-NUS College University of Texas at Austin and [email protected] National University of Singapore [email protected] Zachary Tatlock University of Washington [email protected] arXiv:2003.06458v1 [cs.LO] 13 Mar 2020 Errata for the present version of this paper may be found online at https://proofengineering.org/qed_errata.html The version of record is available at: http://dx.doi.org/10.1561/2500000045 Contents 1 Introduction 103 1.1 Challenges at Scale . 104 1.2 Scope: Domain and Literature . 105 1.3 Overview . 106 1.4 Reading Guide . 106 2 Proof Engineering by Example 108 3 Why Proof Engineering Matters 111 3.1 Proof Engineering for Program Verification . 112 3.2 Proof Engineering for Other Domains . 117 3.3 Practical Impact . 124 4 Foundations and Trusted Bases 126 4.1 Proof Assistant Pre-History . 126 4.2 Proof Assistant Early History . 129 4.3 Proof Assistant Foundations . 130 4.4 Trusted Computing Bases of Proofs and Programs . 137 5 Between the Engineer and the Kernel: Languages and Automation 141 5.1 Styles of Automation . 142 5.2 Automation in Practice . 156 The version of record is available at: http://dx.doi.org/10.1561/2500000045 6 Proof Organization and Scalability 162 6.1 Property Specification and Encodings .