CSE 307: Principles of Programming Languages Spring 2015
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Review Thanks!
CS 242 Thanks! Review Teaching Assistants • Mike Cammarano • TJ Giuli • Hendra Tjahayadi John Mitchell Graders • Andrew Adams Tait Larson Final Exam • Kenny Lau Aman Naimat • Vishal Patel Justin Pettit Wednesday Dec 8 8:30 – 11:30 AM • and more … Gates B01, B03 Course Goals There are many programming languages Understand how programming languages work Early languages Appreciate trade-offs in language design • Fortran, Cobol, APL, ... Be familiar with basic concepts so you can Algol family understand discussions about • Algol 60, Algol 68, Pascal, …, PL/1, … Clu, Ada, Modula, • Language features you haven’t used Cedar/Mesa, ... • Analysis and environment tools Functional languages • Implementation costs and program efficiency • Lisp, FP, SASL, ML, Miranda, Haskell, Scheme, Setl, ... • Language support for program development Object-oriented languages • Smalltalk, Self, Cecil, … • Modula-3, Eiffel, Sather, … • C++, Objective C, …. Java General Themes in this Course Concurrent languages Language provides an abstract view of machine • Actors, Occam, ... • We don’t see registers, length of instruction, etc. • Pai-Lisp, … The right language can make a problem easy; Proprietary and special purpose languages wrong language can make a problem hard • Could have said a lot more about this • TCL, Applescript, Telescript, ... Language design is full of difficult trade-offs • Postscript, Latex, RTF, … • Expressiveness vs efficiency, ... • Domain-specific language • Important to decide what the language is for Specification languages • Every feature -
Functional Programming Is Style of Programming in Which the Basic Method of Computation Is the Application of Functions to Arguments;
PROGRAMMING IN HASKELL Chapter 1 - Introduction 0 What is a Functional Language? Opinions differ, and it is difficult to give a precise definition, but generally speaking: • Functional programming is style of programming in which the basic method of computation is the application of functions to arguments; • A functional language is one that supports and encourages the functional style. 1 Example Summing the integers 1 to 10 in Java: int total = 0; for (int i = 1; i 10; i++) total = total + i; The computation method is variable assignment. 2 Example Summing the integers 1 to 10 in Haskell: sum [1..10] The computation method is function application. 3 Historical Background 1930s: Alonzo Church develops the lambda calculus, a simple but powerful theory of functions. 4 Historical Background 1950s: John McCarthy develops Lisp, the first functional language, with some influences from the lambda calculus, but retaining variable assignments. 5 Historical Background 1960s: Peter Landin develops ISWIM, the first pure functional language, based strongly on the lambda calculus, with no assignments. 6 Historical Background 1970s: John Backus develops FP, a functional language that emphasizes higher-order functions and reasoning about programs. 7 Historical Background 1970s: Robin Milner and others develop ML, the first modern functional language, which introduced type inference and polymorphic types. 8 Historical Background 1970s - 1980s: David Turner develops a number of lazy functional languages, culminating in the Miranda system. 9 Historical Background 1987: An international committee starts the development of Haskell, a standard lazy functional language. 10 Historical Background 1990s: Phil Wadler and others develop type classes and monads, two of the main innovations of Haskell. -
SETL for Internet Data Processing
SETL for Internet Data Processing by David Bacon A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Computer Science New York University January, 2000 Jacob T. Schwartz (Dissertation Advisor) c David Bacon, 1999 Permission to reproduce this work in whole or in part for non-commercial purposes is hereby granted, provided that this notice and the reference http://www.cs.nyu.edu/bacon/phd-thesis/ remain prominently attached to the copied text. Excerpts less than one PostScript page long may be quoted without the requirement to include this notice, but must attach a bibliographic citation that mentions the author’s name, the title and year of this disser- tation, and New York University. For my children ii Acknowledgments First of all, I would like to thank my advisor, Jack Schwartz, for his support and encour- agement. I am also grateful to Ed Schonberg and Robert Dewar for many interesting and helpful discussions, particularly during my early days at NYU. Terry Boult (of Lehigh University) and Richard Wallace have contributed materially to my later work on SETL through grants from the NSF and from ARPA. Finally, I am indebted to my parents, who gave me the strength and will to bring this labor of love to what I hope will be a propitious beginning. iii Preface Colin Broughton, a colleague in Edmonton, Canada, first made me aware of SETL in 1980, when he saw the heavy use I was making of associative tables in SPITBOL for data processing in a protein X-ray crystallography laboratory. -
Verifiable Semantic Difference Languages
Verifiable Semantic Difference Languages Thibaut Girka David Mentré Yann Régis-Gianas Mitsubishi Electric R&D Centre Mitsubishi Electric R&D Centre Univ Paris Diderot, Sorbonne Paris Europe Europe Cité, IRIF/PPS, UMR 8243 CNRS, PiR2, Rennes, France Rennes, France INRIA Paris-Rocquencourt Paris, France ABSTRACT 1 INTRODUCTION Program differences are usually represented as textual differences Let x be a strictly positive natural number. Consider the following on source code with no regard to its syntax or its semantics. In two (almost identical) imperative programs: this paper, we introduce semantic-aware difference languages. A difference denotes a relation between program reduction traces. A s = 0 ; 1 s = 0 ; 1 difference language for the toy imperative programming language d = x ; 2 d = x − 1 ; 2 while (d>0) { 3 while (d>0) { 3 Imp is given as an illustration. i f (x%d== 0) 4 i f (x%d== 0) 4 To certify software evolutions, we want to mechanically verify s = s + d ; 5 s = s + d ; 5 − − that a difference correctly relates two given programs. Product pro- d = d 1 ; 6 d = d 1 ; 6 } 7 } 7 grams and correlating programs are effective proof techniques for s = s − x 8 8 relational reasoning. A product program simulates, in the same programming language as the compared programs, a well-chosen The program P1 (on the left) stores in s the sum of the proper interleaving of their executions to highlight a specific relation be- divisors of the integer x. To that end, it iterates over the integers tween their reduction traces. While this approach enables the use from x to 1 using the variable d and accumulates in the variable s of readily-available static analysis tools on the product program, it the values of d that divide x. -
An Introduction to Programming in Simula
An Introduction to Programming in Simula Rob Pooley This document, including all parts below hyperlinked directly to it, is copyright Rob Pooley ([email protected]). You are free to use it for your own non-commercial purposes, but may not copy it or reproduce all or part of it without including this paragraph. If you wish to use it for gain in any manner, you should contact Rob Pooley for terms appropriate to that use. Teachers in publicly funded schools, universities and colleges are free to use it in their normal teaching. Anyone, including vendors of commercial products, may include links to it in any documentation they distribute, so long as the link is to this page, not any sub-part. This is an .pdf version of the book originally published by Blackwell Scientific Publications. The copyright of that book also belongs to Rob Pooley. REMARK: This document is reassembled from the HTML version found on the web: https://web.archive.org/web/20040919031218/http://www.macs.hw.ac.uk/~rjp/bookhtml/ Oslo 20. March 2018 Øystein Myhre Andersen Table of Contents Chapter 1 - Begin at the beginning Basics Chapter 2 - And end at the end Syntax and semantics of basic elements Chapter 3 - Type cast actors Basic arithmetic and other simple types Chapter 4 - If only Conditional statements Chapter 5 - Would you mind repeating that? Texts and while loops Chapter 6 - Correct Procedures Building blocks Chapter 7 - File FOR future reference Simple input and output using InFile, OutFile and PrintFile Chapter 8 - Item by Item Item oriented reading and writing and for loops Chapter 9 - Classes as Records Chapter 10 - Make me a list Lists 1 - Arrays and simple linked lists Reference comparison Chapter 11 - Like parent like child Sub-classes and complex Boolean expressions Chapter 12 - A Language with Character Character handling, switches and jumps Chapter 13 - Let Us See what We Can See Inspection and Remote Accessing Chapter 14 - Side by Side Coroutines Chapter 15 - File For Immediate Use Direct and Byte Files Chapter 16 - With All My Worldly Goods.. -
Comparative Studies of Programming Languages; Course Lecture Notes
Comparative Studies of Programming Languages, COMP6411 Lecture Notes, Revision 1.9 Joey Paquet Serguei A. Mokhov (Eds.) August 5, 2010 arXiv:1007.2123v6 [cs.PL] 4 Aug 2010 2 Preface Lecture notes for the Comparative Studies of Programming Languages course, COMP6411, taught at the Department of Computer Science and Software Engineering, Faculty of Engineering and Computer Science, Concordia University, Montreal, QC, Canada. These notes include a compiled book of primarily related articles from the Wikipedia, the Free Encyclopedia [24], as well as Comparative Programming Languages book [7] and other resources, including our own. The original notes were compiled by Dr. Paquet [14] 3 4 Contents 1 Brief History and Genealogy of Programming Languages 7 1.1 Introduction . 7 1.1.1 Subreferences . 7 1.2 History . 7 1.2.1 Pre-computer era . 7 1.2.2 Subreferences . 8 1.2.3 Early computer era . 8 1.2.4 Subreferences . 8 1.2.5 Modern/Structured programming languages . 9 1.3 References . 19 2 Programming Paradigms 21 2.1 Introduction . 21 2.2 History . 21 2.2.1 Low-level: binary, assembly . 21 2.2.2 Procedural programming . 22 2.2.3 Object-oriented programming . 23 2.2.4 Declarative programming . 27 3 Program Evaluation 33 3.1 Program analysis and translation phases . 33 3.1.1 Front end . 33 3.1.2 Back end . 34 3.2 Compilation vs. interpretation . 34 3.2.1 Compilation . 34 3.2.2 Interpretation . 36 3.2.3 Subreferences . 37 3.3 Type System . 38 3.3.1 Type checking . 38 3.4 Memory management . -
Numerical Models Show Coral Reefs Can Be Self -Seeding
MARINE ECOLOGY PROGRESS SERIES Vol. 74: 1-11, 1991 Published July 18 Mar. Ecol. Prog. Ser. Numerical models show coral reefs can be self -seeding ' Victorian Institute of Marine Sciences, 14 Parliament Place, Melbourne, Victoria 3002, Australia Australian Institute of Marine Science, PMB No. 3, Townsville MC, Queensland 4810, Australia ABSTRACT: Numerical models are used to simulate 3-dimensional circulation and dispersal of material, such as larvae of marine organisms, on Davies Reef in the central section of Australia's Great Barner Reef. Residence times on and around this reef are determined for well-mixed material and for material which resides at the surface, sea bed and at mid-depth. Results indcate order-of-magnitude differences in the residence times of material at different levels in the water column. They confirm prevlous 2- dimensional modelling which indicated that residence times are often comparable to the duration of the planktonic larval life of many coral reef species. Results reveal a potential for the ma~ntenanceof local populations of vaiious coral reef organisms by self-seeding, and allow reinterpretation of the connected- ness of the coral reef ecosystem. INTRODUCTION persal of particles on reefs. They can be summarised as follows. Hydrodynamic experiments of limited duration Circulation around the reefs is typified by a complex (Ludington 1981, Wolanski & Pickard 1983, Andrews et pattern of phase eddies (Black & Gay 1987a, 1990a). al. 1984) have suggested that flushing times on indi- These eddies and other components of the currents vidual reefs of Australia's Great Barrier Reef (GBR) are interact with the reef bathymetry to create a high level relatively short in comparison with the larval life of of horizontal mixing on and around the reefs (Black many different coral reef species (Yamaguchi 1973, 1988). -
Introduction to Python
Introduction to Python Programming Languages Adapted from Tutorial by Mark Hammond Skippi-Net, Melbourne, Australia [email protected] http://starship.python.net/crew/mhammond What Is Python? Created in 1990 by Guido van Rossum While at CWI, Amsterdam Now hosted by centre for national research initiatives, Reston, VA, USA Free, open source And with an amazing community Object oriented language “Everything is an object” 1 Why Python? Designed to be easy to learn and master Clean, clear syntax Very few keywords Highly portable Runs almost anywhere - high end servers and workstations, down to windows CE Uses machine independent byte-codes Extensible Designed to be extensible using C/C++, allowing access to many external libraries Python: a modern hybrid A language for scripting and prototyping Balance between extensibility and powerful built-in data structures genealogy: Setl (NYU, J.Schwartz et al. 1969-1980) ABC (Amsterdam, Meertens et al. 1980-) Python (Van Rossum et all. 1996-) Very active open-source community 2 Prototyping Emphasis on experimental programming: Interactive (like LISP, ML, etc). Translation to bytecode (like Java) Dynamic typing (like LISP, SETL, APL) Higher-order function (LISP, ML) Garbage-collected, no ptrs (LISP, SNOBOL4) Prototyping Emphasis on experimental programming: Uniform treatment of indexable structures (like SETL) Built-in associative structures (like SETL, SNOBOL4, Postscript) Light syntax, indentation is significant (from ABC) 3 Most obvious and notorious features Clean syntax plus high-level data types Leads to fast coding Uses white-space to delimit blocks Humans generally do, so why not the language? Try it, you will end up liking it Variables do not need declaration Although not a type-less language A Digression on Block Structure There are three ways of dealing with IF structures Sequences of statements with explicit end (Algol-68, Ada, COBOL) Single statement (Algol-60, Pascal, C) Indentation (ABC, Python) 4 Sequence of Statements IF condition THEN stm; stm; . -
Understanding Programming Languages
Understanding Programming Languages M. Ben-Ari Weizmann Institute of Science Originally published by John Wiley & Sons, Chichester, 1996. Copyright °c 2006 by M. Ben-Ari. You may download, display and print one copy for your personal use in non-commercial academic research and teaching. Instructors in non-commerical academic institutions may make one copy for each student in his/her class. All other rights reserved. In particular, posting this document on web sites is prohibited without the express permission of the author. Contents Preface xi I Introduction to Programming Languages 1 1 What Are Programming Languages? 2 1.1 The wrong question . 2 1.2 Imperative languages . 4 1.3 Data-oriented languages . 7 1.4 Object-oriented languages . 11 1.5 Non-imperative languages . 12 1.6 Standardization . 13 1.7 Computer architecture . 13 1.8 * Computability . 16 1.9 Exercises . 17 2 Elements of Programming Languages 18 2.1 Syntax . 18 2.2 * Semantics . 20 2.3 Data . 21 2.4 The assignment statement . 22 2.5 Type checking . 23 2.6 Control statements . 24 2.7 Subprograms . 24 2.8 Modules . 25 2.9 Exercises . 26 v Contents vi 3 Programming Environments 27 3.1 Editor . 28 3.2 Compiler . 28 3.3 Librarian . 30 3.4 Linker . 31 3.5 Loader . 32 3.6 Debugger . 32 3.7 Profiler . 33 3.8 Testing tools . 33 3.9 Configuration tools . 34 3.10 Interpreters . 34 3.11 The Java model . 35 3.12 Exercises . 37 II Essential Concepts 38 4 Elementary Data Types 39 4.1 Integer types . -
Introduction to Python.Pdf
Module 1 - Introduction to Python Introduction to Python What is Python? Python is a multi-paradigm high-level general purpose scripting language. Python supports multiple programming paradigms (models) like structured programming, object- oriented programming, and functional programming. So, it is called a multi-paradigm language. Python is user friendly and can be used to develop application software like text editors, music players, etc. So, it is called a high-level language. Python has several built-in libraries. There are also many third-party libraries. With the help of all these libraries we develop almost anything with Python. So, it is called a general purpose language. Finally, Python programs are executed line by line (interpreted). So, it is called a scripting language. Who Created Python? Python was created by Guido Van Rossum. He worked at Google from 2005-2012. From 2013 onwards he is working at Dropbox. Guido Van Rossum was fond of a British comedian group named Monty Python. Their BBC series was Flying Circus. Rossum named the language after them (Monty Python). History of Python • Python implementation began in 1989. • Python is a successor to ABC language (itself inspired by SETL). • First official release on 20 Feb 1991. • Python 2.0 was released on 16 Oct 2000. • Python 3.0 was released on 3 Dec 2008. • Latest stable release is Python 3.6.1 released in Mar. 2017. Need for Python Programming Following are some of the main reasons for learning Python language: Software quality: Python code is designed to be readable, and hence reusable and maintainable much more than traditional scripting languages. -
The Setl Programmi G La Guage
THE SETL PROGRAMMIG LAGUAGE TAPASYA PATKI POOJA BHANDARI Department of Computer Science University of Arizona Abstract This paper presents a study on the language SETL, which is a very-high level, wide spectrum programming language based on the mathematical theory of sets, primarily used for transformational programming, compiler optimization, algorithm design and data processing. We describe the language features and delve into the issues from the compiler’s perspective. 1 HISTORY In the late 1960s, researchers were addressing compiler optimization problems, and a need for an expressive, high-level language that supported set-intensive algorithm design was felt. SETL, introduced by Prof. Jacob Schwartz at the New York University (NYU) at the Courant Institute of Mathematical Sciences in 1970, was conceived to meet this purpose. Prof. Schwartz, who obtained his PhD from Yale University in 1951, is a renowned mathematician and a computer scientist, and currently holds professorship at NYU. Most of the research and developmental work related to SETL has been carried out at NYU. Modern computer programming languages can be broadly grouped into Object-Oriented languages, Functional programming languages, and Scripting languages. Since SETL was developed during 1970s, SETL does not strictly belong to the above classification. While SETL provides no support for object- orientation, newer versions and dialects of SETL, like SETL2 and ISETL support object oriented and graphics programming. SETL is a very powerful language, and has various application domains. The first ADA translator, ADA/Ed and the first implementation of AIML, the markup language for Artificial Intelligence, were written in it. Python’s predecessor ABC, is also heavily influenced by SETL. -
Object-Oriented Programming in the Beta Programming Language
OBJECT-ORIENTED PROGRAMMING IN THE BETA PROGRAMMING LANGUAGE OLE LEHRMANN MADSEN Aarhus University BIRGER MØLLER-PEDERSEN Ericsson Research, Applied Research Center, Oslo KRISTEN NYGAARD University of Oslo Copyright c 1993 by Ole Lehrmann Madsen, Birger Møller-Pedersen, and Kristen Nygaard. All rights reserved. No part of this book may be copied or distributed without the prior written permission of the authors Preface This is a book on object-oriented programming and the BETA programming language. Object-oriented programming originated with the Simula languages developed at the Norwegian Computing Center, Oslo, in the 1960s. The first Simula language, Simula I, was intended for writing simulation programs. Si- mula I was later used as a basis for defining a general purpose programming language, Simula 67. In addition to being a programming language, Simula1 was also designed as a language for describing and communicating about sys- tems in general. Simula has been used by a relatively small community for many years, although it has had a major impact on research in computer sci- ence. The real breakthrough for object-oriented programming came with the development of Smalltalk. Since then, a large number of programming lan- guages based on Simula concepts have appeared. C++ is the language that has had the greatest influence on the use of object-oriented programming in industry. Object-oriented programming has also been the subject of intensive research, resulting in a large number of important contributions. The authors of this book, together with Bent Bruun Kristensen, have been involved in the BETA project since 1975, the aim of which is to develop con- cepts, constructs and tools for programming.