Specializing Ropes for Ruby
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Traits: Experience with a Language Feature
7UDLWV([SHULHQFHZLWKD/DQJXDJH)HDWXUH (PHUVRQ50XUSK\+LOO $QGUHZ3%ODFN 7KH(YHUJUHHQ6WDWH&ROOHJH 2*,6FKRRORI6FLHQFH1(QJLQHHULQJ$ (YHUJUHHQ3DUNZD\1: 2UHJRQ+HDOWKDQG6FLHQFH8QLYHUVLW\ 2O\PSLD$:$ 1::DONHU5G PXUHPH#HYHUJUHHQHGX %HDYHUWRQ$25 EODFN#FVHRJLHGX ABSTRACT the desired semantics of that method changes, or if a bug is This paper reports our experiences using traits, collections of found, the programmer must track down and fix every copy. By pure methods designed to promote reuse and understandability reusing a method, behavior can be defined and maintained in in object-oriented programs. Traits had previously been used to one place. refactor the Smalltalk collection hierarchy, but only by the crea- tors of traits themselves. This experience report represents the In object-oriented programming, inheritance is the normal way first independent test of these language features. Murphy-Hill of reusing methods—classes inherit methods from other classes. implemented a substantial multi-class data structure called ropes Single inheritance is the most basic and most widespread type of that makes significant use of traits. We found that traits im- inheritance. It allows methods to be shared among classes in an proved understandability and reduced the number of methods elegant and efficient way, but does not always allow for maxi- that needed to be written by 46%. mum reuse. Consider a small example. In Squeak [7], a dialect of Smalltalk, Categories and Subject Descriptors the class &ROOHFWLRQ is the superclass of all the classes that $UUD\ +HDS D.2.3 [Programming Languages]: Coding Tools and Tech- implement collection data structures, including , , 6HW niques - object-oriented programming and . The property of being empty is common to many ob- jects—it simply requires that the object have a size method, and D.3.3 [Programming Languages]: Language Constructs and that the method returns zero. -
Data Structure
EDUSAT LEARNING RESOURCE MATERIAL ON DATA STRUCTURE (For 3rd Semester CSE & IT) Contributors : 1. Er. Subhanga Kishore Das, Sr. Lect CSE 2. Mrs. Pranati Pattanaik, Lect CSE 3. Mrs. Swetalina Das, Lect CA 4. Mrs Manisha Rath, Lect CA 5. Er. Dillip Kumar Mishra, Lect 6. Ms. Supriti Mohapatra, Lect 7. Ms Soma Paikaray, Lect Copy Right DTE&T,Odisha Page 1 Data Structure (Syllabus) Semester & Branch: 3rd sem CSE/IT Teachers Assessment : 10 Marks Theory: 4 Periods per Week Class Test : 20 Marks Total Periods: 60 Periods per Semester End Semester Exam : 70 Marks Examination: 3 Hours TOTAL MARKS : 100 Marks Objective : The effectiveness of implementation of any application in computer mainly depends on the that how effectively its information can be stored in the computer. For this purpose various -structures are used. This paper will expose the students to various fundamentals structures arrays, stacks, queues, trees etc. It will also expose the students to some fundamental, I/0 manipulation techniques like sorting, searching etc 1.0 INTRODUCTION: 04 1.1 Explain Data, Information, data types 1.2 Define data structure & Explain different operations 1.3 Explain Abstract data types 1.4 Discuss Algorithm & its complexity 1.5 Explain Time, space tradeoff 2.0 STRING PROCESSING 03 2.1 Explain Basic Terminology, Storing Strings 2.2 State Character Data Type, 2.3 Discuss String Operations 3.0 ARRAYS 07 3.1 Give Introduction about array, 3.2 Discuss Linear arrays, representation of linear array In memory 3.3 Explain traversing linear arrays, inserting & deleting elements 3.4 Discuss multidimensional arrays, representation of two dimensional arrays in memory (row major order & column major order), and pointers 3.5 Explain sparse matrices. -
Precise Analysis of String Expressions
Precise Analysis of String Expressions Aske Simon Christensen, Anders Møller?, and Michael I. Schwartzbach BRICS??, Department of Computer Science University of Aarhus, Denmark aske,amoeller,mis @brics.dk f g Abstract. We perform static analysis of Java programs to answer a simple question: which values may occur as results of string expressions? The answers are summarized for each expression by a regular language that is guaranteed to contain all possible values. We present several ap- plications of this analysis, including statically checking the syntax of dynamically generated expressions, such as SQL queries. Our analysis constructs flow graphs from class files and generates a context-free gram- mar with a nonterminal for each string expression. The language of this grammar is then widened into a regular language through a variant of an algorithm previously used for speech recognition. The collection of resulting regular languages is compactly represented as a special kind of multi-level automaton from which individual answers may be extracted. If a program error is detected, examples of invalid strings are automat- ically produced. We present extensive benchmarks demonstrating that the analysis is efficient and produces results of useful precision. 1 Introduction To detect errors and perform optimizations in Java programs, it is useful to know which values that may occur as results of string expressions. The exact answer is of course undecidable, so we must settle for a conservative approxima- tion. The answers we provide are summarized for each expression by a regular language that is guaranteed to contain all its possible values. Thus we use an upper approximation, which is what most client analyses will find useful. -
Regular Languages
id+id*id / (id+id)*id - ( id + +id ) +*** * (+)id + Formal languages Strings Languages Grammars 2 Marco Maggini Language Processing Technologies Symbols, Alphabet and strings 3 Marco Maggini Language Processing Technologies String operations 4 • Marco Maggini Language Processing Technologies Languages 5 • Marco Maggini Language Processing Technologies Operations on languages 6 Marco Maggini Language Processing Technologies Concatenation and closure 7 Marco Maggini Language Processing Technologies Closure and linguistic universe 8 Marco Maggini Language Processing Technologies Examples 9 • Marco Maggini Language Processing Technologies Grammars 10 Marco Maggini Language Processing Technologies Production rules L(G) = {anbncn | n ≥ 1} T = {a,b,c} N = {A,B,C} production rules 11 Marco Maggini Language Processing Technologies Derivation 12 Marco Maggini Language Processing Technologies The language of G • Given a grammar G the generated language is the set of strings, made up of terminal symbols, that can be derived in 0 or more steps from the start symbol LG = { x ∈ T* | S ⇒* x} Example: grammar for the arithmetic expressions T = {num,+,*,(,)} start symbol N = {E} S = E E ⇒ E * E ⇒ ( E ) * E ⇒ ( E + E ) * E ⇒ ( num + E ) * E ⇒ (num+num)* E ⇒ (num + num) * num language phrase 13 Marco Maggini Language Processing Technologies Classes of generative grammars 14 Marco Maggini Language Processing Technologies Regular languages • Regular languages can be described by different formal models, that are equivalent ▫ Finite State Automata (FSA) ▫ Regular -
The Cedar Programming Environment: a Midterm Report and Examination
The Cedar Programming Environment: A Midterm Report and Examination Warren Teitelman The Cedar Programming Environment: A Midterm Report and Examination Warren Teitelman t CSL-83-11 June 1984 [P83-00012] © Copyright 1984 Xerox Corporation. All rights reserved. CR Categories and Subject Descriptors: D.2_6 [Software Engineering]: Programming environments. Additional Keywords and Phrases: integrated programming environment, experimental programming, display oriented user interface, strongly typed programming language environment, personal computing. t The author's present address is: Sun Microsystems, Inc., 2550 Garcia Avenue, Mountain View, Ca. 94043. The work described here was performed while employed by Xerox Corporation. XEROX Xerox Corporation Palo Alto Research Center 3333 Coyote Hill Road Palo Alto, California 94304 1 Abstract: This collection of papers comprises a report on Cedar, a state-of-the-art programming system. Cedar combines in a single integrated environment: high-quality graphics, a sophisticated editor and document preparation facility, and a variety of tools for the programmer to use in the construction and debugging of his programs. The Cedar Programming Language is a strongly-typed, compiler-oriented language of the Pascal family. What is especially interesting about the Ce~ar project is that it is one of the few examples where an interactive, experimental programming environment has been built for this kind of language. In the past, such environments have been confined to dynamically typed languages like Lisp and Smalltalk. The first paper, "The Roots of Cedar," describes the conditions in 1978 in the Xerox Palo Alto Research Center's Computer Science Laboratory that led us to embark on the Cedar project and helped to define its objectives and goals. -
Formal Languages
Formal Languages Discrete Mathematical Structures Formal Languages 1 Strings Alphabet: a finite set of symbols – Normally characters of some character set – E.g., ASCII, Unicode – Σ is used to represent an alphabet String: a finite sequence of symbols from some alphabet – If s is a string, then s is its length ¡ ¡ – The empty string is symbolized by ¢ Discrete Mathematical Structures Formal Languages 2 String Operations Concatenation x = hi, y = bye ¡ xy = hibye s ¢ = s = ¢ s ¤ £ ¨© ¢ ¥ § i 0 i s £ ¥ i 1 ¨© s s § i 0 ¦ Discrete Mathematical Structures Formal Languages 3 Parts of a String Prefix Suffix Substring Proper prefix, suffix, or substring Subsequence Discrete Mathematical Structures Formal Languages 4 Language A language is a set of strings over some alphabet ¡ Σ L Examples: – ¢ is a language – ¢ is a language £ ¤ – The set of all legal Java programs – The set of all correct English sentences Discrete Mathematical Structures Formal Languages 5 Operations on Languages Of most concern for lexical analysis Union Concatenation Closure Discrete Mathematical Structures Formal Languages 6 Union The union of languages L and M £ L M s s £ L or s £ M ¢ ¤ ¡ Discrete Mathematical Structures Formal Languages 7 Concatenation The concatenation of languages L and M £ LM st s £ L and t £ M ¢ ¤ ¡ Discrete Mathematical Structures Formal Languages 8 Kleene Closure The Kleene closure of language L ∞ ¡ i L £ L i 0 Zero or more concatenations Discrete Mathematical Structures Formal Languages 9 Positive Closure The positive closure of language L ∞ i L £ L -
Formal Languages
Formal Languages Wednesday, September 29, 2010 Reading: Sipser pp 13-14, 44-45 Stoughton 2.1, 2.2, end of 2.3, beginning of 3.1 CS235 Languages and Automata Department of Computer Science Wellesley College Alphabets, Strings, and Languages An alphabet is a set of symbols. E.g.: 1 = {0,1}; 2 = {-,0,+} 3 = {a,b, …, y, z}; 4 = {, , a , aa } A string over is a seqqfyfuence of symbols from . The empty string is ofte written (Sipser) or ; Stoughton uses %. * denotes all strings over . E.g.: * • 1 contains , 0, 1, 00, 01, 10, 11, 000, … * • 2 contains , -, 0, +, --, -0, -+, 0-, 00, 0+, +-, +0, ++, ---, … * • 3 contains , a, b, …, aa, ab, …, bar, baz, foo, wellesley, … * • 4 contains , , , a , aa, …, a , …, a aa , aa a ,… A lnlanguage over (Stoug hto nns’s -lnlanguage ) is any sbstsubset of *. I.e., it’s a (possibly countably infinite) set of strings over . E.g.: •L1 over 1 is all sequences of 1s and all sequences of 10s. •L2 over 2 is all strings with equal numbers of -, 0, and +. •L3 over 3 is all lowercase words in the OED. •L4 over 4 is {, , a aa }. Formal Languages 11-2 1 Languages over Finite Alphabets are Countable A language = a set of strings over an alphabet = a subset of *. Suppose is finite. Then * (and any subset thereof) is countable. Why? Can enumerate all strings in lexicographic (dictionary) order! • 1 = {0,1} 1 * = {, 0, 1 00, 01, 10, 11 000, 001, 010, 011, 100, 101, 110, 111, …} • for 3 = {a,b, …, y, z}, can enumerate all elements of 3* in lexicographic order -- we’ll eventually get to any given element. -
String Solving with Word Equations and Transducers: Towards a Logic for Analysing Mutation XSS (Full Version)
String Solving with Word Equations and Transducers: Towards a Logic for Analysing Mutation XSS (Full Version) Anthony W. Lin Pablo Barceló Yale-NUS College, Singapore Center for Semantic Web Research [email protected] & Dept. of Comp. Science, Univ. of Chile, Chile [email protected] Abstract to name a few (e.g. see [3] for others). Certain applications, how- We study the fundamental issue of decidability of satisfiability ever, require more expressive constraint languages. The framework over string logics with concatenations and finite-state transducers of satisfiability modulo theories (SMT) builds on top of the basic as atomic operations. Although restricting to one type of opera- constraint satisfaction problem by interpreting atomic propositions tions yields decidability, little is known about the decidability of as a quantifier-free formula in a certain “background” logical the- their combined theory, which is especially relevant when analysing ory, e.g., linear arithmetic. Today fast SMT-solvers supporting a security vulnerabilities of dynamic web pages in a more realis- multitude of background theories are available including Boolector, tic browser model. On the one hand, word equations (string logic CVC4, Yices, and Z3, to name a few (e.g. see [4] for others). The with concatenations) cannot precisely capture sanitisation func- backbones of fast SMT-solvers are usually a highly efficient SAT- tions (e.g. htmlescape) and implicit browser transductions (e.g. in- solver (to handle disjunctions) and an effective method of dealing nerHTML mutations). On the other hand, transducers suffer from with satisfiability of formulas in the background theory. the reverse problem of being able to model sanitisation functions In the past seven years or so there have been a lot of works and browser transductions, but not string concatenations. -
Smalltalk Idioms
Smalltalk Idioms Farewell and a wood pile Kent Beck IT’S THE OBJECTS, STUPID If we parsed the string “@years”, the resulting picture S me awhile to see the obvious. Some- would look like Figure 6. When the BinaryFunction un- times even longer than that. Three or four times in the wraps its children, the right function will be in place. last month I’ve been confronted by problems I had a As I said, several times in the last month I’ve faced hard time solving. In each case, the answer became clear baffling problems that became easy when I asked myself when I asked myself the simple question, “How can I the question, “How could I make an object to solve this make an object to solve this problem for me?” You think problem for me?” Sometimes it was a method that just I’d have figured it out by now: got a problem? make an didn’t want to be simplified, so I created an object just for object for it. that method. Sometimes it was a question of adding Here’s an example: I had to write an editor for a tree features to an object for a particular purpose without clut- structure. There were several ways of viewing and editing tering the object (as in the editing example). I recommend the tree. On the left was a hierarchical list. On the top right that the next time you run into a problem that just doesn’t was a text editor on the currently selected node of the tree. -
A Generalization of Square-Free Strings a GENERALIZATION of SQUARE-FREE STRINGS
A Generalization of Square-free Strings A GENERALIZATION OF SQUARE-FREE STRINGS BY NEERJA MHASKAR, B.Tech., M.S. a thesis submitted to the department of Computing & Software and the school of graduate studies of mcmaster university in partial fulfillment of the requirements for the degree of Doctor of Philosophy c Copyright by Neerja Mhaskar, August, 2016 All Rights Reserved Doctor of Philosophy (2016) McMaster University (Dept. of Computing & Software) Hamilton, Ontario, Canada TITLE: A Generalization of Square-free Strings AUTHOR: Neerja Mhaskar B.Tech., (Mechanical Engineering) Jawaharlal Nehru Technological University Hyderabad, India M.S., (Engineering Science) Louisiana State University, Baton Rouge, USA SUPERVISOR: Professor Michael Soltys CO-SUPERVISOR: Professor Ryszard Janicki NUMBER OF PAGES: xiii, 116 ii To my family Abstract Our research is in the general area of String Algorithms and Combinatorics on Words. Specifically, we study a generalization of square-free strings, shuffle properties of strings, and formalizing the reasoning about finite strings. The existence of infinitely long square-free strings (strings with no adjacent re- peating word blocks) over a three (or more) letter finite set (referred to as Alphabet) is a well-established result. A natural generalization of this problem is that only sub- sets of the alphabet with predefined cardinality are available, while selecting symbols of the square-free string. This problem has been studied by several authors, and the lowest possible bound on the cardinality of the subset given is four. The problem remains open for subset size three and we investigate this question. We show that square-free strings exist in several specialized cases of the problem and propose ap- proaches to solve the problem, ranging from patterns in strings to Proof Complexity. -
Effective STL
Effective STL Author: Scott Meyers E-version is made by: Strangecat@epubcn Thanks is given to j1foo@epubcn, who has helped to revise this e-book. Content Containers........................................................................................1 Item 1. Choose your containers with care........................................................... 1 Item 2. Beware the illusion of container-independent code................................ 4 Item 3. Make copying cheap and correct for objects in containers..................... 9 Item 4. Call empty instead of checking size() against zero. ............................. 11 Item 5. Prefer range member functions to their single-element counterparts... 12 Item 6. Be alert for C++'s most vexing parse................................................... 20 Item 7. When using containers of newed pointers, remember to delete the pointers before the container is destroyed. ........................................................... 22 Item 8. Never create containers of auto_ptrs. ................................................... 27 Item 9. Choose carefully among erasing options.............................................. 29 Item 10. Be aware of allocator conventions and restrictions. ......................... 34 Item 11. Understand the legitimate uses of custom allocators........................ 40 Item 12. Have realistic expectations about the thread safety of STL containers. 43 vector and string............................................................................48 Item 13. Prefer vector -
The Ocaml System Release 4.02
The OCaml system release 4.02 Documentation and user's manual Xavier Leroy, Damien Doligez, Alain Frisch, Jacques Garrigue, Didier R´emy and J´er^omeVouillon August 29, 2014 Copyright © 2014 Institut National de Recherche en Informatique et en Automatique 2 Contents I An introduction to OCaml 11 1 The core language 13 1.1 Basics . 13 1.2 Data types . 14 1.3 Functions as values . 15 1.4 Records and variants . 16 1.5 Imperative features . 18 1.6 Exceptions . 20 1.7 Symbolic processing of expressions . 21 1.8 Pretty-printing and parsing . 22 1.9 Standalone OCaml programs . 23 2 The module system 25 2.1 Structures . 25 2.2 Signatures . 26 2.3 Functors . 27 2.4 Functors and type abstraction . 29 2.5 Modules and separate compilation . 31 3 Objects in OCaml 33 3.1 Classes and objects . 33 3.2 Immediate objects . 36 3.3 Reference to self . 37 3.4 Initializers . 38 3.5 Virtual methods . 38 3.6 Private methods . 40 3.7 Class interfaces . 42 3.8 Inheritance . 43 3.9 Multiple inheritance . 44 3.10 Parameterized classes . 44 3.11 Polymorphic methods . 47 3.12 Using coercions . 50 3.13 Functional objects . 54 3.14 Cloning objects . 55 3.15 Recursive classes . 58 1 2 3.16 Binary methods . 58 3.17 Friends . 60 4 Labels and variants 63 4.1 Labels . 63 4.2 Polymorphic variants . 69 5 Advanced examples with classes and modules 73 5.1 Extended example: bank accounts . 73 5.2 Simple modules as classes .