Programming in Standard ML ’97: a Tutorial Introduction
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Type-Safe Multi-Tier Programming with Standard ML Modules
Experience Report: Type-Safe Multi-Tier Programming with Standard ML Modules Martin Elsman Philip Munksgaard Ken Friis Larsen Department of Computer Science, iAlpha AG, Switzerland Department of Computer Science, University of Copenhagen, Denmark [email protected] University of Copenhagen, Denmark [email protected] kfl[email protected] Abstract shared module, it is now possible, on the server, to implement a We describe our experience with using Standard ML modules and module that for each function responds to a request by first deseri- Standard ML’s core language typing features for ensuring type- alising the argument into a value, calls the particular function and safety across physically separated tiers in an extensive web applica- replies to the client with a serialised version of the result value. tion built around a database-backed server platform and advanced Dually, on the client side, for each function, the client code can client code that accesses the server through asynchronous server serialise the argument and send the request asynchronously to the requests. server. When the server responds, the client will deserialise the re- sult value and apply the handler function to the value. Both for the server part and for the client part, the code can be implemented in 1. Introduction a type-safe fashion. In particular, if an interface type changes, the The iAlpha Platform is an advanced asset management system programmer will be notified about all type-inconsistencies at com- featuring a number of analytics modules for combining trading pile -
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 ................................................................................................................................................................................. -
A System of Constructor Classes: Overloading and Implicit Higher-Order Polymorphism
A system of constructor classes: overloading and implicit higher-order polymorphism Mark P. Jones Yale University, Department of Computer Science, P.O. Box 2158 Yale Station, New Haven, CT 06520-2158. [email protected] Abstract range of other data types, as illustrated by the following examples: This paper describes a flexible type system which combines overloading and higher-order polymorphism in an implicitly data Tree a = Leaf a | Tree a :ˆ: Tree a typed language using a system of constructor classes – a mapTree :: (a → b) → (Tree a → Tree b) natural generalization of type classes in Haskell. mapTree f (Leaf x) = Leaf (f x) We present a wide range of examples which demonstrate mapTree f (l :ˆ: r) = mapTree f l :ˆ: mapTree f r the usefulness of such a system. In particular, we show how data Opt a = Just a | Nothing constructor classes can be used to support the use of monads mapOpt :: (a → b) → (Opt a → Opt b) in a functional language. mapOpt f (Just x) = Just (f x) The underlying type system permits higher-order polymor- mapOpt f Nothing = Nothing phism but retains many of many of the attractive features that have made the use of Hindley/Milner type systems so Each of these functions has a similar type to that of the popular. In particular, there is an effective algorithm which original map and also satisfies the functor laws given above. can be used to calculate principal types without the need for With this in mind, it seems a shame that we have to use explicit type or kind annotations. -
Python Programming
Python Programming Wikibooks.org June 22, 2012 On the 28th of April 2012 the contents of the English as well as German Wikibooks and Wikipedia projects were licensed under Creative Commons Attribution-ShareAlike 3.0 Unported license. An URI to this license is given in the list of figures on page 149. If this document is a derived work from the contents of one of these projects and the content was still licensed by the project under this license at the time of derivation this document has to be licensed under the same, a similar or a compatible license, as stated in section 4b of the license. The list of contributors is included in chapter Contributors on page 143. The licenses GPL, LGPL and GFDL are included in chapter Licenses on page 153, since this book and/or parts of it may or may not be licensed under one or more of these licenses, and thus require inclusion of these licenses. The licenses of the figures are given in the list of figures on page 149. This PDF was generated by the LATEX typesetting software. The LATEX source code is included as an attachment (source.7z.txt) in this PDF file. To extract the source from the PDF file, we recommend the use of http://www.pdflabs.com/tools/pdftk-the-pdf-toolkit/ utility or clicking the paper clip attachment symbol on the lower left of your PDF Viewer, selecting Save Attachment. After extracting it from the PDF file you have to rename it to source.7z. To uncompress the resulting archive we recommend the use of http://www.7-zip.org/. -
Understanding the Syntactic Rule Usage in Java
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by UCL Discovery Understanding the Syntactic Rule Usage in Java Dong Qiua, Bixin Lia,∗, Earl T. Barrb, Zhendong Suc aSchool of Computer Science and Engineering, Southeast University, China bDepartment of Computer Science, University College London, UK cDepartment of Computer Science, University of California Davis, USA Abstract Context: Syntax is fundamental to any programming language: syntax defines valid programs. In the 1970s, computer scientists rigorously and empirically studied programming languages to guide and inform language design. Since then, language design has been artistic, driven by the aesthetic concerns and intuitions of language architects. Despite recent studies on small sets of selected language features, we lack a comprehensive, quantitative, empirical analysis of how modern, real-world source code exercises the syntax of its programming language. Objective: This study aims to understand how programming language syntax is employed in actual development and explore their potential applications based on the results of syntax usage analysis. Method: We present our results on the first such study on Java, a modern, mature, and widely-used programming language. Our corpus contains over 5,000 open-source Java projects, totalling 150 million source lines of code (SLoC). We study both independent (i.e. applications of a single syntax rule) and dependent (i.e. applications of multiple syntax rules) rule usage, and quantify their impact over time and project size. Results: Our study provides detailed quantitative information and yields insight, particularly (i) confirming the conventional wisdom that the usage of syntax rules is Zipfian; (ii) showing that the adoption of new rules and their impact on the usage of pre-existing rules vary significantly over time; and (iii) showing that rule usage is highly contextual. -
S-Algol Reference Manual Ron Morrison
S-algol Reference Manual Ron Morrison University of St. Andrews, North Haugh, Fife, Scotland. KY16 9SS CS/79/1 1 Contents Chapter 1. Preface 2. Syntax Specification 3. Types and Type Rules 3.1 Universe of Discourse 3.2 Type Rules 4. Literals 4.1 Integer Literals 4.2 Real Literals 4.3 Boolean Literals 4.4 String Literals 4.5 Pixel Literals 4.6 File Literal 4.7 pntr Literal 5. Primitive Expressions and Operators 5.1 Boolean Expressions 5.2 Comparison Operators 5.3 Arithmetic Expressions 5.4 Arithmetic Precedence Rules 5.5 String Expressions 5.6 Picture Expressions 5.7 Pixel Expressions 5.8 Precedence Table 5.9 Other Expressions 6. Declarations 6.1 Identifiers 6.2 Variables, Constants and Declaration of Data Objects 6.3 Sequences 6.4 Brackets 6.5 Scope Rules 7. Clauses 7.1 Assignment Clause 7.2 if Clause 7.3 case Clause 7.4 repeat ... while ... do ... Clause 7.5 for Clause 7.6 abort Clause 8. Procedures 8.1 Declarations and Calls 8.2 Forward Declarations 2 9. Aggregates 9.1 Vectors 9.1.1 Creation of Vectors 9.1.2 upb and lwb 9.1.3 Indexing 9.1.4 Equality and Equivalence 9.2 Structures 9.2.1 Creation of Structures 9.2.2 Equality and Equivalence 9.2.3 Indexing 9.3 Images 9.3.1 Creation of Images 9.3.2 Indexing 9.3.3 Depth Selection 9.3.4 Equality and Equivalence 10. Input and Output 10.1 Input 10.2 Output 10.3 i.w, s.w and r.w 10.4 End of File 11. -
Refinement Types for ML
Refinement Types for ML Tim Freeman Frank Pfenning [email protected] [email protected] School of Computer Science School of Computer Science Carnegie Mellon University Carnegie Mellon University Pittsburgh, Pennsylvania 15213-3890 Pittsburgh, Pennsylvania 15213-3890 Abstract tended to the full Standard ML language. To see the opportunity to improve ML’s type system, We describe a refinement of ML’s type system allow- consider the following function which returns the last ing the specification of recursively defined subtypes of cons cell in a list: user-defined datatypes. The resulting system of refine- ment types preserves desirable properties of ML such as datatype α list = nil | cons of α * α list decidability of type inference, while at the same time fun lastcons (last as cons(hd,nil)) = last allowing more errors to be detected at compile-time. | lastcons (cons(hd,tl)) = lastcons tl The type system combines abstract interpretation with We know that this function will be undefined when ideas from the intersection type discipline, but remains called on an empty list, so we would like to obtain a closely tied to ML in that refinement types are given type error at compile-time when lastcons is called with only to programs which are already well-typed in ML. an argument of nil. Using refinement types this can be achieved, thus preventing runtime errors which could be 1 Introduction caught at compile-time. Similarly, we would like to be able to write code such as Standard ML [MTH90] is a practical programming lan- case lastcons y of guage with higher-order functions, polymorphic types, cons(x,nil) => print x and a well-developed module system. -
Sugarj: Library-Based Syntactic Language Extensibility
SugarJ: Library-based Syntactic Language Extensibility Sebastian Erdweg, Tillmann Rendel, Christian Kastner,¨ and Klaus Ostermann University of Marburg, Germany Abstract import pair.Sugar; Existing approaches to extend a programming language with syntactic sugar often leave a bitter taste, because they cannot public class Test f be used with the same ease as the main extension mechanism private (String, Integer) p = ("12", 34); of the programming language—libraries. Sugar libraries are g a novel approach for syntactically extending a programming Figure 1. Using a sugar library for pairs. language within the language. A sugar library is like an or- dinary library, but can, in addition, export syntactic sugar for using the library. Sugar libraries maintain the compos- 1. Introduction ability and scoping properties of ordinary libraries and are Bridging the gap between domain concepts and the imple- hence particularly well-suited for embedding a multitude of mentation of these concepts in a programming language is domain-specific languages into a host language. They also one of the “holy grails” of software development. Domain- inherit self-applicability from libraries, which means that specific languages (DSLs), such as regular expressions for sugar libraries can provide syntactic extensions for the defi- the domain of text recognition or Java Server Pages for the nition of other sugar libraries. domain of dynamic web pages have often been proposed To demonstrate the expressiveness and applicability of to address this problem [31]. To use DSLs in large soft- sugar libraries, we have developed SugarJ, a language on ware systems that touch multiple domains, developers have top of Java, SDF and Stratego, which supports syntactic to be able to compose multiple domain-specific languages extensibility. -
Four Lectures on Standard ML
Mads Tofte March Lab oratory for Foundations of Computer Science Department of Computer Science Edinburgh University Four Lectures on Standard ML The following notes give an overview of Standard ML with emphasis placed on the Mo dules part of the language The notes are to the b est of my knowledge faithful to The Denition of Standard ML Version as regards syntax semantics and terminology They have b een written so as to b e indep endent of any particular implemen tation The exercises in the rst lectures can b e tackled without the use of a machine although having access to an implementation will no doubt b e b enecial The pro ject in Lecture presupp oses access to an implementation of the full language including mo dules At present the Edinburgh compiler do es not fall into this category the author used the New Jersey Standard ML compiler Lecture gives an introduction to ML aimed at the reader who is familiar with some programming language but do es not know ML Both the Core Language and the Mo dules are covered by way of example Lecture discusses the use of ML mo dules in the development of large programs A useful metho dology for programming with functors signatures and structures is presented Lecture gives a fairly detailed account of the static semantics of ML mo dules for those who really want to understand the crucial notions of sharing and signature matching Lecture presents a one day pro ject intended to give the student an opp ortunity of mo difying a nontrivial piece of software using functors signatures and structures -
Lecture Slides
Outline Meta-Classes Guy Wiener Introduction AOP Classes Generation 1 Introduction Meta-Classes in Python Logging 2 Meta-Classes in Python Delegation Meta-Classes vs. Traditional OOP 3 Meta-Classes vs. Traditional OOP Outline Meta-Classes Guy Wiener Introduction AOP Classes Generation 1 Introduction Meta-Classes in Python Logging 2 Meta-Classes in Python Delegation Meta-Classes vs. Traditional OOP 3 Meta-Classes vs. Traditional OOP What is Meta-Programming? Meta-Classes Definition Guy Wiener Meta-Program A program that: Introduction AOP Classes One of its inputs is a program Generation (possibly itself) Meta-Classes in Python Its output is a program Logging Delegation Meta-Classes vs. Traditional OOP Meta-Programs Nowadays Meta-Classes Guy Wiener Introduction AOP Classes Generation Compilers Meta-Classes in Python Code Generators Logging Delegation Model-Driven Development Meta-Classes vs. Traditional Templates OOP Syntactic macros (Lisp-like) Meta-Classes The Problem With Static Programming Meta-Classes Guy Wiener Introduction AOP Classes Generation Meta-Classes How to share features between classes and class hierarchies? in Python Logging Share static attributes Delegation Meta-Classes Force classes to adhere to the same protocol vs. Traditional OOP Share code between similar methods Meta-Classes Meta-Classes Guy Wiener Introduction AOP Classes Definition Generation Meta-Classes in Python Meta-Class A class that creates classes Logging Delegation Objects that are instances of the same class Meta-Classes share the same behavior vs. Traditional OOP Classes that are instances of the same meta-class share the same behavior Meta-Classes Meta-Classes Guy Wiener Introduction AOP Classes Definition Generation Meta-Classes in Python Meta-Class A class that creates classes Logging Delegation Objects that are instances of the same class Meta-Classes share the same behavior vs. -
Understanding and Evolving the ML Module System
Understanding and Evolving the ML Module System Derek Dreyer May 2005 CMU-CS-05-131 School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Thesis Committee: Robert Harper (co-chair) Karl Crary (co-chair) Peter Lee David MacQueen (University of Chicago) Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Copyright c 2005 Derek Dreyer This research was sponsored in part by the National Science Foundation under grant CCR-0121633 and EIA- 9706572, and the US Air Force under grant F19628-95-C-0050 and a generous fellowship. The views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either expressed or implied, of any sponsoring institution, the U.S. government or any other entity. Keywords: ML, module systems, type systems, functors, abstract data types, lambda calculus, recursive modules, singleton kinds Abstract The ML module system stands as a high-water mark of programming language support for data abstraction. Nevertheless, it is not in a fully evolved state. One prominent weakness is that module interdependencies in ML are restricted to be acyclic, which means that mutually recursive functions and data types must be written in the same module even if they belong conceptually in different modules. Existing efforts to remedy this limitation either involve drastic changes to the notion of what a module is, or fail to allow mutually recursive modules to hide type information from one another. Another issue is that there are several dialects of ML, and the module systems of these dialects differ in subtle yet semantically significant ways that have been difficult to account for in any rigorous way. -
Syntactic Sugar Programming Languages' Constructs - Preliminary Study
ICIT 2011 The 5th International Conference on Information Technology Syntactic Sugar Programming Languages' Constructs - Preliminary Study #1 #2 Mustafa Al-Tamim , Rashid Jayousi Department of Computer Science, Al-Quds University, Jerusalem, Palestine [email protected] 00972-59-9293002 [email protected] 00972-52-7456731 ICIT 2011 The 5th International Conference on Information Technology Syntactic Sugar Programming Languages' Constructs - Preliminary Study #1 #2 Mustafa Al-Tamim , Rashid Jayousi Department of Computer Science, Al-Quds University, Jerusalem, Palestine [email protected] [email protected] Abstract— Software application development is a daily task done syntax and help in making it more readable, easier to write, by developers and code writer all over the world. Valuable less syntax errors and less ambiguous. portion of developers’ time is spent in writing repetitive In this research, we proposed new set of syntactic sugar keywords, debugging code, trying to understand its semantic, constructs that can be composed by a mixture of existing and fixing syntax errors. These tasks become harder when no constructs obtained from some programming languages in integrated development environment (IDE) is available or developers use remote access terminals like UNIX and simple addition to syntactic enhancements suggested by us. Through text editors for code writing. Syntactic sugar constructs in our work as software developer, team leaders, and guiding programming languages are found to offer simple and easy many students in their projects, we noticed that developers syntax constructs to make developers life easier and smother. In write a lot of repetitive keywords in specific parts of code like this paper, we propose a new set of syntactic sugar constructs, packages calling keywords, attributes access modifiers, code and try to find if they really can help developers in eliminating segments and building blocks’ scopes determination symbols syntax errors, make code more readable, more easier to write, and others.