An Extensible Type System for Component-Based Design
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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. -
Safety Properties Liveness Properties
Computer security Goal: prevent bad things from happening PLDI’06 Tutorial T1: Clients not paying for services Enforcing and Expressing Security Critical service unavailable with Programming Languages Confidential information leaked Important information damaged System used to violate laws (e.g., copyright) Andrew Myers Conventional security mechanisms aren’t Cornell University up to the challenge http://www.cs.cornell.edu/andru PLDI Tutorial: Enforcing and Expressing Security with Programming Languages - Andrew Myers 2 Harder & more important Language-based security In the ’70s, computing systems were isolated. Conventional security: program is black box software updates done infrequently by an experienced Encryption administrator. Firewalls you trusted the (few) programs you ran. System calls/privileged mode physical access was required. Process-level privilege and permissions-based access control crashes and outages didn’t cost billions. The Internet has changed all of this. Prevents addressing important security issues: Downloaded and mobile code we depend upon the infrastructure for everyday services you have no idea what programs do. Buffer overruns and other safety problems software is constantly updated – sometimes without your knowledge Extensible systems or consent. Application-level security policies a hacker in the Philippines is as close as your neighbor. System-level security validation everything is executable (e.g., web pages, email). Languages and compilers to the rescue! PLDI Tutorial: Enforcing -
Abstract Data Types
Chapter 2 Abstract Data Types The second idea at the core of computer science, along with algorithms, is data. In a modern computer, data consists fundamentally of binary bits, but meaningful data is organized into primitive data types such as integer, real, and boolean and into more complex data structures such as arrays and binary trees. These data types and data structures always come along with associated operations that can be done on the data. For example, the 32-bit int data type is defined both by the fact that a value of type int consists of 32 binary bits but also by the fact that two int values can be added, subtracted, multiplied, compared, and so on. An array is defined both by the fact that it is a sequence of data items of the same basic type, but also by the fact that it is possible to directly access each of the positions in the list based on its numerical index. So the idea of a data type includes a specification of the possible values of that type together with the operations that can be performed on those values. An algorithm is an abstract idea, and a program is an implementation of an algorithm. Similarly, it is useful to be able to work with the abstract idea behind a data type or data structure, without getting bogged down in the implementation details. The abstraction in this case is called an \abstract data type." An abstract data type specifies the values of the type, but not how those values are represented as collections of bits, and it specifies operations on those values in terms of their inputs, outputs, and effects rather than as particular algorithms or program code. -
Cyclone: a Type-Safe Dialect of C∗
Cyclone: A Type-Safe Dialect of C∗ Dan Grossman Michael Hicks Trevor Jim Greg Morrisett If any bug has achieved celebrity status, it is the • In C, an array of structs will be laid out contigu- buffer overflow. It made front-page news as early ously in memory, which is good for cache locality. as 1987, as the enabler of the Morris worm, the first In Java, the decision of how to lay out an array worm to spread through the Internet. In recent years, of objects is made by the compiler, and probably attacks exploiting buffer overflows have become more has indirections. frequent, and more virulent. This year, for exam- ple, the Witty worm was released to the wild less • C has data types that match hardware data than 48 hours after a buffer overflow vulnerability types and operations. Java abstracts from the was publicly announced; in 45 minutes, it infected hardware (“write once, run anywhere”). the entire world-wide population of 12,000 machines running the vulnerable programs. • C has manual memory management, whereas Notably, buffer overflows are a problem only for the Java has garbage collection. Garbage collec- C and C++ languages—Java and other “safe” lan- tion is safe and convenient, but places little con- guages have built-in protection against them. More- trol over performance in the hands of the pro- over, buffer overflows appear in C programs written grammer, and indeed encourages an allocation- by expert programmers who are security concious— intensive style. programs such as OpenSSH, Kerberos, and the com- In short, C programmers can see the costs of their mercial intrusion detection programs that were the programs simply by looking at them, and they can target of Witty. -
Cs164: Introduction to Programming Languages and Compilers
Lecture 19 Flow Analysis flow analysis in prolog; applications of flow analysis Ras Bodik Hack Your Language! CS164: Introduction to Programming Ali and Mangpo Languages and Compilers, Spring 2013 UC Berkeley 1 Today Static program analysis what it computes and how are its results used Points-to analysis analysis for understanding flow of pointer values Andersen’s algorithm approximation of programs: how and why Andersen’s algorithm in Prolog points-to analysis expressed via just four deduction rules Andersen’s algorithm via CYK parsing (optional) expressed as CYK parsing of a graph representing the pgm Static Analysis of Programs definition and motivation 3 Static program analysis Computes program properties used by a range of clients: compiler optimizers, static debuggers, security audit tools, IDEs, … Static analysis == at compile time – that is, prior to seeing the actual input ==> analysis answer must be correct for all possible inputs Sample program properties: does variable x has a constant value (for all inputs)? does foo() return a table (whenever called, on all inputs)? 4 Client 1: Program Optimization Optimize program by finding constant subexpressions Ex: replace x[i] with x[1] if we know that i is always 1. This optimizations saves the address computation. This analysis is called constant propagation i = 2 … i = i+2 … if (…) { …} … x[i] = x[i-1] 5 Client 2: Find security vulnerabilities One specific analysis: find broken sanitization In a web server program, as whether a value can flow from POST (untrusted source) to the SQL interpreter (trusted sink) without passing through the cgi.escape() sanitizer? This is taint analysis. -
5. Data Types
IEEE FOR THE FUNCTIONAL VERIFICATION LANGUAGE e Std 1647-2011 5. Data types The e language has a number of predefined data types, including the integer and Boolean scalar types common to most programming languages. In addition, new scalar data types (enumerated types) that are appropriate for programming, modeling hardware, and interfacing with hardware simulators can be created. The e language also provides a powerful mechanism for defining OO hierarchical data structures (structs) and ordered collections of elements of the same type (lists). The following subclauses provide a basic explanation of e data types. 5.1 e data types Most e expressions have an explicit data type, as follows: — Scalar types — Scalar subtypes — Enumerated scalar types — Casting of enumerated types in comparisons — Struct types — Struct subtypes — Referencing fields in when constructs — List types — The set type — The string type — The real type — The external_pointer type — The “untyped” pseudo type Certain expressions, such as HDL objects, have no explicit data type. See 5.2 for information on how these expressions are handled. 5.1.1 Scalar types Scalar types in e are one of the following: numeric, Boolean, or enumerated. Table 17 shows the predefined numeric and Boolean types. Both signed and unsigned integers can be of any size and, thus, of any range. See 5.1.2 for information on how to specify the size and range of a scalar field or variable explicitly. See also Clause 4. 5.1.2 Scalar subtypes A scalar subtype can be named and created by using a scalar modifier to specify the range or bit width of a scalar type. -
Lecture 2: Variables and Primitive Data Types
Lecture 2: Variables and Primitive Data Types MIT-AITI Kenya 2005 1 In this lecture, you will learn… • What a variable is – Types of variables – Naming of variables – Variable assignment • What a primitive data type is • Other data types (ex. String) MIT-Africa Internet Technology Initiative 2 ©2005 What is a Variable? • In basic algebra, variables are symbols that can represent values in formulas. • For example the variable x in the formula f(x)=x2+2 can represent any number value. • Similarly, variables in computer program are symbols for arbitrary data. MIT-Africa Internet Technology Initiative 3 ©2005 A Variable Analogy • Think of variables as an empty box that you can put values in. • We can label the box with a name like “Box X” and re-use it many times. • Can perform tasks on the box without caring about what’s inside: – “Move Box X to Shelf A” – “Put item Z in box” – “Open Box X” – “Remove contents from Box X” MIT-Africa Internet Technology Initiative 4 ©2005 Variables Types in Java • Variables in Java have a type. • The type defines what kinds of values a variable is allowed to store. • Think of a variable’s type as the size or shape of the empty box. • The variable x in f(x)=x2+2 is implicitly a number. • If x is a symbol representing the word “Fish”, the formula doesn’t make sense. MIT-Africa Internet Technology Initiative 5 ©2005 Java Types • Integer Types: – int: Most numbers you’ll deal with. – long: Big integers; science, finance, computing. – short: Small integers. -
Csci 658-01: Software Language Engineering Python 3 Reflexive
CSci 658-01: Software Language Engineering Python 3 Reflexive Metaprogramming Chapter 3 H. Conrad Cunningham 4 May 2018 Contents 3 Decorators and Metaclasses 2 3.1 Basic Function-Level Debugging . .2 3.1.1 Motivating example . .2 3.1.2 Abstraction Principle, staying DRY . .3 3.1.3 Function decorators . .3 3.1.4 Constructing a debug decorator . .4 3.1.5 Using the debug decorator . .6 3.1.6 Case study review . .7 3.1.7 Variations . .7 3.1.7.1 Logging . .7 3.1.7.2 Optional disable . .8 3.2 Extended Function-Level Debugging . .8 3.2.1 Motivating example . .8 3.2.2 Decorators with arguments . .9 3.2.3 Prefix decorator . .9 3.2.4 Reformulated prefix decorator . 10 3.3 Class-Level Debugging . 12 3.3.1 Motivating example . 12 3.3.2 Class-level debugger . 12 3.3.3 Variation: Attribute access debugging . 14 3.4 Class Hierarchy Debugging . 16 3.4.1 Motivating example . 16 3.4.2 Review of objects and types . 17 3.4.3 Class definition process . 18 3.4.4 Changing the metaclass . 20 3.4.5 Debugging using a metaclass . 21 3.4.6 Why metaclasses? . 22 3.5 Chapter Summary . 23 1 3.6 Exercises . 23 3.7 Acknowledgements . 23 3.8 References . 24 3.9 Terms and Concepts . 24 Copyright (C) 2018, H. Conrad Cunningham Professor of Computer and Information Science University of Mississippi 211 Weir Hall P.O. Box 1848 University, MS 38677 (662) 915-5358 Note: This chapter adapts David Beazley’s debugly example presentation from his Python 3 Metaprogramming tutorial at PyCon’2013 [Beazley 2013a]. -
Subtyping Recursive Types
ACM Transactions on Programming Languages and Systems, 15(4), pp. 575-631, 1993. Subtyping Recursive Types Roberto M. Amadio1 Luca Cardelli CNRS-CRIN, Nancy DEC, Systems Research Center Abstract We investigate the interactions of subtyping and recursive types, in a simply typed λ-calculus. The two fundamental questions here are whether two (recursive) types are in the subtype relation, and whether a term has a type. To address the first question, we relate various definitions of type equivalence and subtyping that are induced by a model, an ordering on infinite trees, an algorithm, and a set of type rules. We show soundness and completeness between the rules, the algorithm, and the tree semantics. We also prove soundness and a restricted form of completeness for the model. To address the second question, we show that to every pair of types in the subtype relation we can associate a term whose denotation is the uniquely determined coercion map between the two types. Moreover, we derive an algorithm that, when given a term with implicit coercions, can infer its least type whenever possible. 1This author's work has been supported in part by Digital Equipment Corporation and in part by the Stanford-CNR Collaboration Project. Page 1 Contents 1. Introduction 1.1 Types 1.2 Subtypes 1.3 Equality of Recursive Types 1.4 Subtyping of Recursive Types 1.5 Algorithm outline 1.6 Formal development 2. A Simply Typed λ-calculus with Recursive Types 2.1 Types 2.2 Terms 2.3 Equations 3. Tree Ordering 3.1 Subtyping Non-recursive Types 3.2 Folding and Unfolding 3.3 Tree Expansion 3.4 Finite Approximations 4. -
An Operational Semantics and Type Safety Proof for Multiple Inheritance in C++
An Operational Semantics and Type Safety Proof for Multiple Inheritance in C++ Daniel Wasserrab Tobias Nipkow Gregor Snelting Frank Tip Universitat¨ Passau Technische Universitat¨ Universitat¨ Passau IBM T.J. Watson Research [email protected] Munchen¨ [email protected] Center passau.de [email protected] passau.de [email protected] Abstract pected type, or end with an exception. The semantics and proof are formalized and machine-checked using the Isabelle/HOL theorem We present an operational semantics and type safety proof for 1 multiple inheritance in C++. The semantics models the behaviour prover [15] and are available online . of method calls, field accesses, and two forms of casts in C++ class One of the main sources of complexity in C++ is a complex hierarchies exactly, and the type safety proof was formalized and form of multiple inheritance, in which a combination of shared machine-checked in Isabelle/HOL. Our semantics enables one, for (“virtual”) and repeated (“nonvirtual”) inheritance is permitted. Be- the first time, to understand the behaviour of operations on C++ cause of this complexity, the behaviour of operations on C++ class class hierarchies without referring to implementation-level artifacts hierarchies has traditionally been defined informally [29], and in such as virtual function tables. Moreover, it can—as the semantics terms of implementation-level constructs such as v-tables. We are is executable—act as a reference for compilers, and it can form only aware of a few formal treatments—and of no operational the basis for more advanced correctness proofs of, e.g., automated semantics—for C++-like languages with shared and repeated mul- program transformations. -
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 . -
Chapter 8 Automation Using Powershell
Chapter 8 Automation Using PowerShell Virtual Machine Manager is one of the first Microsoft software products to fully adopt Windows PowerShell and offer its users a complete management interface tailored for script- ing. From the first release of VMM 2007, the Virtual Machine Manager Administrator Console was written on top of Windows PowerShell, utilizing the many cmdlets that VMM offers. This approach made VMM very extensible and partner friendly and allows customers to accomplish anything that VMM offers in the Administrator Console via scripts and automation. Windows PowerShell is also the only public application programming interface (API) that VMM offers, giving both developers and administrators a single point of reference for managing VMM. Writing scripts that interface with VMM, Hyper-V, or Virtual Server can be made very easy using Windows PowerShell’s support for WMI, .NET, and COM. In this chapter, you will learn to: ◆ Describe the main benefits that PowerShell offers for VMM ◆ Use the VMM PowerShell cmdlets ◆ Create scheduled PowerShell scripts VMM and Windows PowerShell System Center Virtual Machine Manager (VMM) 2007, the first release of VMM, was one of the first products to develop its entire graphical user interface (the VMM Administrator Con- sole) on top of Windows PowerShell (previously known as Monad). This approach proved very advantageous for customers that wanted all of the VMM functionality to be available through some form of an API. The VMM team made early bets on Windows PowerShell as its public management interface, and they have not been disappointed with the results. With its consis- tent grammar, the great integration with .NET, and the abundance of cmdlets, PowerShell is quickly becoming the management interface of choice for enterprise applications.