5. Data Types
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Type-Safe Composition of Object Modules*
International Conference on Computer Systems and Education I ISc Bangalore Typ esafe Comp osition of Ob ject Mo dules Guruduth Banavar Gary Lindstrom Douglas Orr Department of Computer Science University of Utah Salt LakeCity Utah USA Abstract Intro duction It is widely agreed that strong typing in We describ e a facility that enables routine creases the reliability and eciency of soft typ echecking during the linkage of exter ware However compilers for statically typ ed nal declarations and denitions of separately languages suchasC and C in tradi compiled programs in ANSI C The primary tional nonintegrated programming environ advantage of our serverstyle typ echecked ments guarantee complete typ esafety only linkage facility is the ability to program the within a compilation unit but not across comp osition of ob ject mo dules via a suite of suchunits Longstanding and widely avail strongly typ ed mo dule combination op era able linkers comp ose separately compiled tors Such programmability enables one to units bymatching symb ols purely byname easily incorp orate programmerdened data equivalence with no regard to their typ es format conversion stubs at linktime In ad Such common denominator linkers accom dition our linkage facility is able to automat mo date ob ject mo dules from various source ically generate safe co ercion stubs for com languages by simply ignoring the static se patible encapsulated data mantics of the language Moreover com monly used ob ject le formats are not de signed to incorp orate source language typ e -
Integers in C: an Open Invitation to Security Attacks?
Integers In C: An Open Invitation To Security Attacks? Zack Coker Samir Hasan Jeffrey Overbey [email protected] [email protected] [email protected] Munawar Hafiz Christian Kästnery [email protected] Auburn University yCarnegie Mellon University Auburn, AL, USA Pittsburgh, PA, USA ABSTRACT integer type is assigned to a location with a smaller type, We performed an empirical study to explore how closely e.g., assigning an int value to a short variable. well-known, open source C programs follow the safe C stan- Secure coding standards, such as MISRA's Guidelines for dards for integer behavior, with the goal of understanding the use of the C language in critical systems [37] and CERT's how difficult it is to migrate legacy code to these stricter Secure Coding in C and C++ [44], identify these issues that standards. We performed an automated analysis on fifty-two are otherwise allowed (and sometimes partially defined) in releases of seven C programs (6 million lines of preprocessed the C standard and impose restrictions on the use of inte- C code), as well as releases of Busybox and Linux (nearly gers. This is because most of the integer issues are benign. one billion lines of partially-preprocessed C code). We found But, there are two cases where the issues may cause serious that integer issues, that are allowed by the C standard but problems. One is in safety-critical sections, where untrapped not by the safer C standards, are ubiquitous|one out of four integer issues can lead to unexpected runtime behavior. An- integers were inconsistently declared, and one out of eight other is in security-critical sections, where integer issues can integers were inconsistently used. -
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. -
Understanding Integer Overflow in C/C++
Appeared in Proceedings of the 34th International Conference on Software Engineering (ICSE), Zurich, Switzerland, June 2012. Understanding Integer Overflow in C/C++ Will Dietz,∗ Peng Li,y John Regehr,y and Vikram Adve∗ ∗Department of Computer Science University of Illinois at Urbana-Champaign fwdietz2,[email protected] ySchool of Computing University of Utah fpeterlee,[email protected] Abstract—Integer overflow bugs in C and C++ programs Detecting integer overflows is relatively straightforward are difficult to track down and may lead to fatal errors or by using a modified compiler to insert runtime checks. exploitable vulnerabilities. Although a number of tools for However, reliable detection of overflow errors is surprisingly finding these bugs exist, the situation is complicated because not all overflows are bugs. Better tools need to be constructed— difficult because overflow behaviors are not always bugs. but a thorough understanding of the issues behind these errors The low-level nature of C and C++ means that bit- and does not yet exist. We developed IOC, a dynamic checking tool byte-level manipulation of objects is commonplace; the line for integer overflows, and used it to conduct the first detailed between mathematical and bit-level operations can often be empirical study of the prevalence and patterns of occurrence quite blurry. Wraparound behavior using unsigned integers of integer overflows in C and C++ code. Our results show that intentional uses of wraparound behaviors are more common is legal and well-defined, and there are code idioms that than is widely believed; for example, there are over 200 deliberately use it. -
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. -
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/. -
Signedness-Agnostic Program Analysis: Precise Integer Bounds for Low-Level Code
Signedness-Agnostic Program Analysis: Precise Integer Bounds for Low-Level Code Jorge A. Navas, Peter Schachte, Harald Søndergaard, and Peter J. Stuckey Department of Computing and Information Systems, The University of Melbourne, Victoria 3010, Australia Abstract. Many compilers target common back-ends, thereby avoid- ing the need to implement the same analyses for many different source languages. This has led to interest in static analysis of LLVM code. In LLVM (and similar languages) most signedness information associated with variables has been compiled away. Current analyses of LLVM code tend to assume that either all values are signed or all are unsigned (except where the code specifies the signedness). We show how program analysis can simultaneously consider each bit-string to be both signed and un- signed, thus improving precision, and we implement the idea for the spe- cific case of integer bounds analysis. Experimental evaluation shows that this provides higher precision at little extra cost. Our approach turns out to be beneficial even when all signedness information is available, such as when analysing C or Java code. 1 Introduction The “Low Level Virtual Machine” LLVM is rapidly gaining popularity as a target for compilers for a range of programming languages. As a result, the literature on static analysis of LLVM code is growing (for example, see [2, 7, 9, 11, 12]). LLVM IR (Intermediate Representation) carefully specifies the bit- width of all integer values, but in most cases does not specify whether values are signed or unsigned. This is because, for most operations, two’s complement arithmetic (treating the inputs as signed numbers) produces the same bit-vectors as unsigned arithmetic. -
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]. -
Chapter 5: Substitutes for C Constructs
CHAPTER 5 Substitutes for C Constructs THE Java programming language shares many similarities with the C program- ming language, but several C constructs have been omitted. In most cases, it’s obvi- ous why a C construct was omitted and how to make do without it. This chapter suggests replacements for several omitted C constructs whose replacements are not so obvious. The common thread that connects the items in this chapter is that all of the omitted constructs are data-oriented rather than object-oriented. The Java pro- gramming language provides a powerful type system, and the suggested replace- ments take full advantage of that type system to deliver a higher quality abstraction than the C constructs they replace. Even if you choose to skip this chapter, it’s probably worth reading Item 21, which discusses the typesafe enum pattern, a replacement for C’s enum construct. This pattern is not widely known at the time of this writing, and it has several advan- tages over the methods currently in common use. Item 19: Replace structures with classes The C struct construct was omitted from the Java programming language because a class does everything a structure does and more. A structure merely groups multi- ple data fields into a single object; a class associates operations with the resulting object and allows the data fields to be hidden from users of the object. In other words, a class can encapsulate its data into an object that is accessed solely by its methods, allowing the implementor the freedom to change the representation over time (Item 12). -
Occam 2.1 Reference Manual
occam 2.1 reference manual SGS-THOMSON Microelectronics Limited May 12, 1995 iv occam 2.1 REFERENCE MANUAL SGS-THOMSON Microelectronics Limited First published 1988 by Prentice Hall International (UK) Ltd as the occam 2 Reference Manual. SGS-THOMSON Microelectronics Limited 1995. SGS-THOMSON Microelectronics reserves the right to make changes in specifications at any time and without notice. The information furnished by SGS-THOMSON Microelectronics in this publication is believed to be accurate, but no responsibility is assumed for its use, nor for any infringement of patents or other rights of third parties resulting from its use. No licence is granted under any patents, trademarks or other rights of SGS-THOMSON Microelectronics. The INMOS logo, INMOS, IMS and occam are registered trademarks of SGS-THOMSON Microelectronics Limited. Document number: 72 occ 45 03 All rights reserved. No part of this publication may be reproduced, stored in a retrival system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without prior permission, in writing, from SGS-THOMSON Microelectronics Limited. Contents Contents v Contents overview ix Preface xi Introduction 1 Syntax and program format 3 1 Primitive processes 5 1.1 Assignment 5 1.2 Communication 6 1.3 SKIP and STOP 7 2 Constructed processes 9 2.1 Sequence 9 2.2 Conditional 11 2.3 Selection 13 2.4 WHILE loop 14 2.5 Parallel 15 2.6 Alternation 19 2.7 Processes 24 3 Data types 25 3.1 Primitive data types 25 3.2 Named data types 26 3.3 Literals -
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. -
Floating-Point Exception Handing
ISO/IEC JTC1/SC22/WG5 N1372 Working draft of ISO/IEC TR 15580, second edition Information technology ± Programming languages ± Fortran ± Floating-point exception handing This page to be supplied by ISO. No changes from first edition, except for for mechanical things such as dates. i ISO/IEC TR 15580 : 1998(E) Draft second edition, 13th August 1999 ISO/IEC Foreword To be supplied by ISO. No changes from first edition, except for for mechanical things such as dates. ii ISO/IEC Draft second edition, 13th August 1999 ISO/IEC TR 15580: 1998(E) Introduction Exception handling is required for the development of robust and efficient numerical software. In particular, it is necessary in order to be able to write portable scientific libraries. In numerical Fortran programming, current practice is to employ whatever exception handling mechanisms are provided by the system/vendor. This clearly inhibits the production of fully portable numerical libraries and programs. It is particularly frustrating now that IEEE arithmetic (specified by IEEE 754-1985 Standard for binary floating-point arithmetic, also published as IEC 559:1989, Binary floating-point arithmetic for microprocessor systems) is so widely used, since built into it are the five conditions: overflow, invalid, divide-by-zero, underflow, and inexact. Our aim is to provide support for these conditions. We have taken the opportunity to provide support for other aspects of the IEEE standard through a set of elemental functions that are applicable only to IEEE data types. This proposal involves three standard modules: IEEE_EXCEPTIONS contains a derived type, some named constants of this type, and some simple procedures.