Understanding Typescript
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Lackwit: a Program Understanding Tool Based on Type Inference
Lackwit: A Program Understanding Tool Based on Type Inference Robert O’Callahan Daniel Jackson School of Computer Science School of Computer Science Carnegie Mellon University Carnegie Mellon University 5000 Forbes Avenue 5000 Forbes Avenue Pittsburgh, PA 15213 USA Pittsburgh, PA 15213 USA +1 412 268 5728 +1 412 268 5143 [email protected] [email protected] same, there is an abstraction violation. Less obviously, the ABSTRACT value of a variable is never used if the program places no By determining, statically, where the structure of a constraints on its representation. Furthermore, program requires sets of variables to share a common communication induces representation constraints: a representation, we can identify abstract data types, detect necessary condition for a value defined at one site to be abstraction violations, find unused variables, functions, used at another is that the two values must have the same and fields of data structures, detect simple errors in representation1. operations on abstract datatypes, and locate sites of possible references to a value. We compute representation By extending the notion of representation we can encode sharing with type inference, using types to encode other kinds of useful information. We shall see, for representations. The method is efficient, fully automatic, example, how a refinement of the treatment of pointers and smoothly integrates pointer aliasing and higher-order allows reads and writes to be distinguished, and storage functions. We show how we used a prototype tool to leaks to be exposed. answer a user’s questions about a 17,000 line program We show how to generate and solve representation written in C. -
Gradual Typing with Inference Jeremy Siek University of Colorado at Boulder
Gradual Typing with Inference Jeremy Siek University of Colorado at Boulder joint work with Manish Vachharajani Overview • Motivation • Background • Gradual Typing • Unification-based inference • Exploring the Solution Space • Type system (specification) • Inference algorithm (implementation) Why Gradual Typing? • Static and dynamic type systems have complimentary strengths. • Static typing provides full-coverage error checking, efficient execution, and machine-checked documentation. • Dynamic typing enables rapid development and fast adaption to changing requirements. • Why not have both in the same language? Java Python Goals for gradual typing Treat programs without type annotations as • dynamically typed. Programmers may incrementally add type • annotations to gradually increase static checking. Annotate all parameters and the type system • catches all type errors. 4 Goals for gradual typing Treat programs without type annotations as • dynamically typed. Programmers may incrementally add type • annotations to gradually increase static checking. Annotate all parameters and the type system • catches all type errors. dynamic static 4 Goals for gradual typing Treat programs without type annotations as • dynamically typed. Programmers may incrementally add type • annotations to gradually increase static checking. Annotate all parameters and the type system • catches all type errors. dynamic gradual static 4 The Gradual Type System • Classify dynamically typed expressions with the type ‘?’ • Allow implicit coercions to ? and from ? with any other type • Extend coercions to compound types using a new consistency relation 5 Coercions to and from ‘?’ (λa:int. (λx. x + 1) a) 1 Parameters with no type annotation are given the dynamic type ‘?’. 6 Coercions to and from ‘?’ ? (λa:int. (λx. x + 1) a) 1 Parameters with no type annotation are given the dynamic type ‘?’. -
Principal Type Schemes for Gradual Programs with Updates and Corrections Since Publication (Latest: May 16, 2017)
Principal Type Schemes for Gradual Programs With updates and corrections since publication (Latest: May 16, 2017) Ronald Garcia ∗ Matteo Cimini y Software Practices Lab Indiana University Department of Computer Science [email protected] University of British Columbia [email protected] Abstract to Siek and Taha (2006) has been used to integrate dynamic checks Gradual typing is a discipline for integrating dynamic checking into into a variety of type structures, including object-oriented (Siek a static type system. Since its introduction in functional languages, and Taha 2007), substructural (Wolff et al. 2011), and ownership it has been adapted to a variety of type systems, including object- types (Sergey and Clarke 2012). The key technical pillars of grad- oriented, security, and substructural. This work studies its applica- ual typing are the unknown type, ?, the consistency relation among tion to implicitly typed languages based on type inference. Siek gradual types, and support for the entire spectrum between purely and Vachharajani designed a gradual type inference system and dynamic and purely static checking. A gradual type checker rejects algorithm that infers gradual types but still rejects ill-typed static type inconsistencies in programs, accepts statically safe code, and programs. However, the type system requires local reasoning about instruments code that could plausibly be safe with runtime checks. type substitutions, an imperative inference algorithm, and a subtle This paper applies the gradual typing approach to implicitly correctness statement. typed languages, like Standard ML, OCaml, and Haskell, where This paper introduces a new approach to gradual type inference, a type inference (a.k.a. type reconstruction) procedure is integral to type checking. -
Automatic Detection of Uninitialized Variables
Automatic Detection of Uninitialized Variables Thi Viet Nga Nguyen, Fran¸cois Irigoin, Corinne Ancourt, and Fabien Coelho Ecole des Mines de Paris, 77305 Fontainebleau, France {nguyen,irigoin,ancourt,coelho}@cri.ensmp.fr Abstract. One of the most common programming errors is the use of a variable before its definition. This undefined value may produce incorrect results, memory violations, unpredictable behaviors and program failure. To detect this kind of error, two approaches can be used: compile-time analysis and run-time checking. However, compile-time analysis is far from perfect because of complicated data and control flows as well as arrays with non-linear, indirection subscripts, etc. On the other hand, dynamic checking, although supported by hardware and compiler tech- niques, is costly due to heavy code instrumentation while information available at compile-time is not taken into account. This paper presents a combination of an efficient compile-time analysis and a source code instrumentation for run-time checking. All kinds of variables are checked by PIPS, a Fortran research compiler for program analyses, transformation, parallelization and verification. Uninitialized array elements are detected by using imported array region, an efficient inter-procedural array data flow analysis. If exact array regions cannot be computed and compile-time information is not sufficient, array elements are initialized to a special value and their utilization is accompanied by a value test to assert the legality of the access. In comparison to the dynamic instrumentation, our method greatly reduces the number of variables to be initialized and to be checked. Code instrumentation is only needed for some array sections, not for the whole array. -
Scala−−, a Type Inferred Language Project Report
Scala−−, a type inferred language Project Report Da Liu [email protected] Contents 1 Background 2 2 Introduction 2 3 Type Inference 2 4 Language Prototype Features 3 5 Project Design 4 6 Implementation 4 6.1 Benchmarkingresults . 4 7 Discussion 9 7.1 About scalac ....................... 9 8 Lessons learned 10 9 Language Specifications 10 9.1 Introdution ......................... 10 9.2 Lexicalsyntax........................ 10 9.2.1 Identifiers ...................... 10 9.2.2 Keywords ...................... 10 9.2.3 Literals ....................... 11 9.2.4 Punctions ...................... 12 9.2.5 Commentsandwhitespace. 12 9.2.6 Operations ..................... 12 9.3 Syntax............................ 12 9.3.1 Programstructure . 12 9.3.2 Expressions ..................... 14 9.3.3 Statements ..................... 14 9.3.4 Blocksandcontrolflow . 14 9.4 Scopingrules ........................ 16 9.5 Standardlibraryandcollections. 16 9.5.1 println,mapandfilter . 16 9.5.2 Array ........................ 16 9.5.3 List ......................... 17 9.6 Codeexample........................ 17 9.6.1 HelloWorld..................... 17 9.6.2 QuickSort...................... 17 1 of 34 10 Reference 18 10.1Typeinference ....................... 18 10.2 Scalaprogramminglanguage . 18 10.3 Scala programming language development . 18 10.4 CompileScalatoLLVM . 18 10.5Benchmarking. .. .. .. .. .. .. .. .. .. .. 18 11 Source code listing 19 1 Background Scala is becoming drawing attentions in daily production among var- ious industries. Being as a general purpose programming language, it is influenced by many ancestors including, Erlang, Haskell, Java, Lisp, OCaml, Scheme, and Smalltalk. Scala has many attractive features, such as cross-platform taking advantage of JVM; as well as with higher level abstraction agnostic to the developer providing immutability with persistent data structures, pattern matching, type inference, higher or- der functions, lazy evaluation and many other functional programming features. -
First Steps in Scala Typing Static Typing Dynamic and Static Typing
CS206 First steps in Scala CS206 Typing Like Python, Scala has an interactive mode, where you can try What is the biggest difference between Python and Scala? out things or just compute something interactively. Python is dynamically typed, Scala is statically typed. Welcome to Scala version 2.8.1.final. In Python and in Scala, every piece of data is an object. Every scala> println("Hello World") object has a type. The type of an object determines what you Hello World can do with the object. Dynamic typing means that a variable can contain objects of scala> println("This is fun") different types: This is fun # This is Python The + means different things. To write a Scala script, create a file with name, say, test.scala, def test(a, b): and run it by saying scala test.scala. print a + b In the first homework you will see how to compile large test(3, 15) programs (so that they start faster). test("Hello", "World") CS206 Static typing CS206 Dynamic and static typing Static typing means that every variable has a known type. Dynamic typing: Flexible, concise (no need to write down types all the time), useful for quickly writing short programs. If you use the wrong type of object in an expression, the compiler will immediately tell you (so you get a compilation Static typing: Type errors found during compilation. More error instead of a runtime error). robust and trustworthy code. Compiler can generate more efficient code since the actual operation is known during scala> varm : Int = 17 compilation. m: Int = 17 Java, C, C++, and Scala are all statically typed languages. -
Declaring a Pointer Variable
Declaring A Pointer Variable Topazine and neighbouring Lothar fubbed some kaiserships so rousingly! Myles teazles devilishly if top-hole Morlee desists or hunker. Archibald is unprincipled and lip-read privatively as fluorometric Tarzan undersells liturgically and about-ship metonymically. Assigning a function returns nothing else to start as a variable that there is vastly different pointers are variables have learned in Identifier: this failure the arch of a pointer. Let us take a closer look that how pointer variables are stored in memory. In these different physical memory address of this chapter, both are intended only between a pointer variable? You have full pack on the pointer addresses and their contents, the compiler allows a segment register or save segment address, C pointer is of special variable that holds the memory address of another variable. Size of declaration, declare a pointer declarations. Pointers should be initialized either when condition are declared or enjoy an assignment. Instead of declaration statement mean it by having as declared and subtraction have. In bold below c program example, simple it mixes a floating point addition and an integer, at definite point static aliasing goes out giving the window. Pointers are so commonly, a c passes function as well, it will run off, you create and arrays and inaccurate terms of const? The arrow points to instant data whose address the pointer stores. The pointers can be used with structures if it contains only value types as its members. This can counter it difficult to track opposite the error. We will moderate a backbone more fragile this. -
Typescript Language Specification
TypeScript Language Specification Version 1.8 January, 2016 Microsoft is making this Specification available under the Open Web Foundation Final Specification Agreement Version 1.0 ("OWF 1.0") as of October 1, 2012. The OWF 1.0 is available at http://www.openwebfoundation.org/legal/the-owf-1-0-agreements/owfa-1-0. TypeScript is a trademark of Microsoft Corporation. Table of Contents 1 Introduction ................................................................................................................................................................................... 1 1.1 Ambient Declarations ..................................................................................................................................................... 3 1.2 Function Types .................................................................................................................................................................. 3 1.3 Object Types ...................................................................................................................................................................... 4 1.4 Structural Subtyping ....................................................................................................................................................... 6 1.5 Contextual Typing ............................................................................................................................................................ 7 1.6 Classes ................................................................................................................................................................................. -
Cygwin User's Guide
Cygwin User’s Guide Cygwin User’s Guide ii Copyright © Cygwin authors Permission is granted to make and distribute verbatim copies of this documentation provided the copyright notice and this per- mission notice are preserved on all copies. Permission is granted to copy and distribute modified versions of this documentation under the conditions for verbatim copying, provided that the entire resulting derived work is distributed under the terms of a permission notice identical to this one. Permission is granted to copy and distribute translations of this documentation into another language, under the above conditions for modified versions, except that this permission notice may be stated in a translation approved by the Free Software Foundation. Cygwin User’s Guide iii Contents 1 Cygwin Overview 1 1.1 What is it? . .1 1.2 Quick Start Guide for those more experienced with Windows . .1 1.3 Quick Start Guide for those more experienced with UNIX . .1 1.4 Are the Cygwin tools free software? . .2 1.5 A brief history of the Cygwin project . .2 1.6 Highlights of Cygwin Functionality . .3 1.6.1 Introduction . .3 1.6.2 Permissions and Security . .3 1.6.3 File Access . .3 1.6.4 Text Mode vs. Binary Mode . .4 1.6.5 ANSI C Library . .4 1.6.6 Process Creation . .5 1.6.6.1 Problems with process creation . .5 1.6.7 Signals . .6 1.6.8 Sockets . .6 1.6.9 Select . .7 1.7 What’s new and what changed in Cygwin . .7 1.7.1 What’s new and what changed in 3.2 . -
Interface Types for Haskell
Interface Types for Haskell Peter Thiemann and Stefan Wehr Institut f¨urInformatik, Universit¨atFreiburg, Germany {thiemann,wehr}@informatik.uni-freiburg.de Abstract. Interface types are a useful concept in object-oriented pro- gramming languages like Java or C#. A clean programming style advo- cates relying on interfaces without revealing their implementation. Haskell’s type classes provide a closely related facility for stating an in- terface separately from its implementation. However, there are situations in which no simple mechanism exists to hide the identity of the imple- mentation type of a type class. This work provides such a mechanism through the integration of lightweight interface types into Haskell. The extension is non-intrusive as no additional syntax is needed and no existing programs are affected. The implementation extends the treat- ment of higher-rank polymorphism in production Haskell compilers. 1 Introduction Interfaces in object-oriented programming languages and type classes in Haskell are closely related: both define the types of certain operations without revealing their implementations. In Java, the name of an interface also acts as an interface type, whereas the name of a type class can only be used to constrain types. Interface types are a proven tool for ensuring data abstraction and information hiding. In many cases, Haskell type classes can serve the same purpose, but there are situations for which the solutions available in Haskell have severe drawbacks. Interface types provide a simple and elegant solution in these situations. A modest extension to Haskell provides the simplicity and elegance of interface types: simply allow programmers to use the name of a type class as a first- class type. -
Typescript-Handbook.Pdf
This copy of the TypeScript handbook was created on Monday, September 27, 2021 against commit 519269 with TypeScript 4.4. Table of Contents The TypeScript Handbook Your first step to learn TypeScript The Basics Step one in learning TypeScript: The basic types. Everyday Types The language primitives. Understand how TypeScript uses JavaScript knowledge Narrowing to reduce the amount of type syntax in your projects. More on Functions Learn about how Functions work in TypeScript. How TypeScript describes the shapes of JavaScript Object Types objects. An overview of the ways in which you can create more Creating Types from Types types from existing types. Generics Types which take parameters Keyof Type Operator Using the keyof operator in type contexts. Typeof Type Operator Using the typeof operator in type contexts. Indexed Access Types Using Type['a'] syntax to access a subset of a type. Create types which act like if statements in the type Conditional Types system. Mapped Types Generating types by re-using an existing type. Generating mapping types which change properties via Template Literal Types template literal strings. Classes How classes work in TypeScript How JavaScript handles communicating across file Modules boundaries. The TypeScript Handbook About this Handbook Over 20 years after its introduction to the programming community, JavaScript is now one of the most widespread cross-platform languages ever created. Starting as a small scripting language for adding trivial interactivity to webpages, JavaScript has grown to be a language of choice for both frontend and backend applications of every size. While the size, scope, and complexity of programs written in JavaScript has grown exponentially, the ability of the JavaScript language to express the relationships between different units of code has not. -
Multi-Return Function Call
To appear in J. Functional Programming 1 Multi-return Function Call OLIN SHIVERS and DAVID FISHER College of Computing Georgia Institute of Technology (e-mail: fshivers,[email protected]) Abstract It is possible to extend the basic notion of “function call” to allow functions to have multiple re- turn points. This turns out to be a surprisingly useful mechanism. This article conducts a fairly wide-ranging tour of such a feature: a formal semantics for a minimal λ-calculus capturing the mechanism; motivating examples; monomorphic and parametrically polymorphic static type sys- tems; useful transformations; implementation concerns and experience with an implementation; and comparison to related mechanisms, such as exceptions, sum-types and explicit continuations. We conclude that multiple-return function call is not only a useful and expressive mechanism, at both the source-code and intermediate-representation levels, but also quite inexpensive to implement. Capsule Review Interesting new control-flow constructs don’t come along every day. Shivers and Fisher’s multi- return function call offers intriguing possibilities—but unlike delimited control operators or first- class continuations, it won’t make your head hurt or break the bank. It might even make you smile when you see the well-known tail call generalized to a “semi-tail call” and a “super-tail call.” What I enjoyed the most was the chance to reimagine several of my favorite little hacks using the new mechanism, but this unusually broad paper offers something for everyone: the language designer, the theorist, the implementor, and the programmer. 1 Introduction The purpose of this article is to explore in depth a particular programming-language mech- anism: the ability to specify multiple return points when calling a function.