25. Object-Oriented Lisp
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Metaobject Protocols: Why We Want Them and What Else They Can Do
Metaobject protocols: Why we want them and what else they can do Gregor Kiczales, J.Michael Ashley, Luis Rodriguez, Amin Vahdat, and Daniel G. Bobrow Published in A. Paepcke, editor, Object-Oriented Programming: The CLOS Perspective, pages 101 ¾ 118. The MIT Press, Cambridge, MA, 1993. © Massachusetts Institute of Technology All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher. Metaob ject Proto cols WhyWeWant Them and What Else They Can Do App ears in Object OrientedProgramming: The CLOS Perspective c Copyright 1993 MIT Press Gregor Kiczales, J. Michael Ashley, Luis Ro driguez, Amin Vahdat and Daniel G. Bobrow Original ly conceivedasaneat idea that could help solve problems in the design and implementation of CLOS, the metaobject protocol framework now appears to have applicability to a wide range of problems that come up in high-level languages. This chapter sketches this wider potential, by drawing an analogy to ordinary language design, by presenting some early design principles, and by presenting an overview of three new metaobject protcols we have designed that, respectively, control the semantics of Scheme, the compilation of Scheme, and the static paral lelization of Scheme programs. Intro duction The CLOS Metaob ject Proto col MOP was motivated by the tension b etween, what at the time, seemed liketwo con icting desires. The rst was to have a relatively small but p owerful language for doing ob ject-oriented programming in Lisp. The second was to satisfy what seemed to b e a large numb er of user demands, including: compatibility with previous languages, p erformance compara- ble to or b etter than previous implementations and extensibility to allow further exp erimentation with ob ject-oriented concepts see Chapter 2 for examples of directions in which ob ject-oriented techniques might b e pushed. -
MANNING Greenwich (74° W
Object Oriented Perl Object Oriented Perl DAMIAN CONWAY MANNING Greenwich (74° w. long.) For electronic browsing and ordering of this and other Manning books, visit http://www.manning.com. The publisher offers discounts on this book when ordered in quantity. For more information, please contact: Special Sales Department Manning Publications Co. 32 Lafayette Place Fax: (203) 661-9018 Greenwich, CT 06830 email: [email protected] ©2000 by Manning Publications Co. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by means electronic, mechanical, photocopying, or otherwise, without prior written permission of the publisher. Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in the book, and Manning Publications was aware of a trademark claim, the designations have been printed in initial caps or all caps. Recognizing the importance of preserving what has been written, it is Manning’s policy to have the books we publish printed on acid-free paper, and we exert our best efforts to that end. Library of Congress Cataloging-in-Publication Data Conway, Damian, 1964- Object oriented Perl / Damian Conway. p. cm. includes bibliographical references. ISBN 1-884777-79-1 (alk. paper) 1. Object-oriented programming (Computer science) 2. Perl (Computer program language) I. Title. QA76.64.C639 1999 005.13'3--dc21 99-27793 CIP Manning Publications Co. Copyeditor: Adrianne Harun 32 Lafayette -
A Comparative Study on the Effect of Multiple Inheritance Mechanism in Java, C++, and Python on Complexity and Reusability of Code
(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 8, No. 6, 2017 A Comparative Study on the Effect of Multiple Inheritance Mechanism in Java, C++, and Python on Complexity and Reusability of Code Fawzi Albalooshi Amjad Mahmood Department of Computer Science Department of Computer Science University of Bahrain University of Bahrain Kingdom of Bahrain Kingdom of Bahrain Abstract—Two of the fundamental uses of generalization in Booch, there are two problems associated with multiple object-oriented software development are the reusability of code inheritance and they are how to deal with name collisions from and better structuring of the description of objects. Multiple super classes, and how to handle repeated inheritance. He inheritance is one of the important features of object-oriented presents solutions to these two problems. Other researchers [4] methodologies which enables developers to combine concepts and suggest that there is a real need for multiple inheritance for increase the reusability of the resulting software. However, efficient object implementation. They justify their claim multiple inheritance is implemented differently in commonly referring to the lack of multiple subtyping in the ADA 95 used programming languages. In this paper, we use Chidamber revision which was considered as a deficiency that was and Kemerer (CK) metrics to study the complexity and rectified in the newer version [5]. It is clear that multiple reusability of multiple inheritance as implemented in Python, inheritance is a fundamental concept in object-orientation. The Java, and C++. The analysis of results suggests that out of the three languages investigated Python and C++ offer better ability to incorporate multiple inheritance in system design and reusability of software when using multiple inheritance, whereas implementation will better structure the description of objects Java has major deficiencies when implementing multiple modeling, their natural status and enabling further code reuse inheritance resulting in poor structure of objects. -
Mixins and Traits
◦ ◦◦◦ TECHNISCHE UNIVERSITAT¨ MUNCHEN¨ ◦◦◦◦ ◦ ◦ ◦◦◦ ◦◦◦◦ ¨ ¨ ◦ ◦◦ FAKULTAT FUR INFORMATIK Programming Languages Mixins and Traits Dr. Michael Petter Winter 2016/17 What advanced techiques are there besides multiple implementation inheritance? Outline Design Problems Cons of Implementation Inheritance 1 Inheritance vs Aggregation 1 2 (De-)Composition Problems Lack of finegrained Control 2 Inappropriate Hierarchies Inheritance in Detail A Focus on Traits 1 A Model for single inheritance 1 2 Inheritance Calculus with Separation of Composition and Inheritance Expressions Modeling 2 3 Modeling Mixins Trait Calculus Mixins in Languages Traits in Languages 1 (Virtual) Extension Methods 1 Simulating Mixins 2 Squeak 2 Native Mixins Reusability ≡ Inheritance? Codesharing in Object Oriented Systems is often inheritance-centric. Inheritance itself comes in different flavours: I single inheritance I multiple inheritance All flavours of inheritance tackle problems of decomposition and composition The Adventure Game Door ShortDoor LockedDoor canPass(Person p) canOpen(Person p) ? ShortLockedDoor canOpen(Person p) canPass(Person p) The Adventure Game Door <interface>Doorlike canPass(Person p) canOpen(Person p) Short canPass(Person p) Locked canOpen(Person p) ShortLockedDoor ! Aggregation & S.-Inheritance Door must explicitely provide canOpen(Person p) chaining canPass(Person p) Doorlike must anticipate wrappers ) Multiple Inheritance X The Wrapper FileStream SocketStream read() read() write() write() ? SynchRW acquireLock() releaseLock() ! Inappropriate Hierarchies -
Towards Practical Runtime Type Instantiation
Towards Practical Runtime Type Instantiation Karl Naden Carnegie Mellon University [email protected] Abstract 2. Dispatch Problem Statement Symmetric multiple dispatch, generic functions, and variant type In contrast with traditional dynamic dispatch, the runtime of a lan- parameters are powerful language features that have been shown guage with multiple dispatch cannot necessarily use any static in- to aid in modular and extensible library design. However, when formation about the call site provided by the typechecker. It must symmetric dispatch is applied to generic functions, type parameters check if there exists a more specific function declaration than the may have to be instantiated as a part of dispatch. Adding variant one statically chosen that has the same name and is applicable to generics increases the complexity of type instantiation, potentially the runtime types of the provided arguments. The process for deter- making it prohibitively expensive. We present a syntactic restriction mining when a function declaration is more specific than another is on generic functions and an algorithm designed and implemented for laid out in [4]. A fully instantiated function declaration is applicable the Fortress programming language that simplifies the computation if the runtime types of the arguments are subtypes of the declared required at runtime when these features are combined. parameter types. To show applicability for a generic function we need to find an instantiation that witnesses the applicability and the type safety of the function call so that it can be used by the code. Formally, given 1. Introduction 1. a function signature f X <: τX (y : τy): τr, J K Symmetric multiple dispatch brings with it several benefits for 2. -
Balancing the Eulisp Metaobject Protocol
Balancing the EuLisp Metaob ject Proto col x y x z Harry Bretthauer and Harley Davis and Jurgen Kopp and Keith Playford x German National Research Center for Computer Science GMD PO Box W Sankt Augustin FRG y ILOG SA avenue Gallieni Gentilly France z Department of Mathematical Sciences University of Bath Bath BA AY UK techniques which can b e used to solve them Op en questions Abstract and unsolved problems are presented to direct future work The challenge for the metaob ject proto col designer is to bal One of the main problems is to nd a b etter balance ance the conicting demands of eciency simplicity and b etween expressiveness and ease of use on the one hand extensibili ty It is imp ossible to know all desired extensions and eciency on the other in advance some of them will require greater functionality Since the authors of this pap er and other memb ers while others require greater eciency In addition the pro of the EuLisp committee have b een engaged in the design to col itself must b e suciently simple that it can b e fully and implementation of an ob ject system with a metaob ject do cumented and understo o d by those who need to use it proto col for EuLisp Padget and Nuyens intended This pap er presents a metaob ject proto col for EuLisp to correct some of the p erceived aws in CLOS to sim which provides expressiveness by a multileveled proto col plify it without losing any of its p ower and to provide the and achieves eciency by static semantics for predened means to more easily implement it eciently The current metaob -
Mixin-Based Programming in C++1
Mixin-Based Programming in C++1 Yannis Smaragdakis Don Batory College of Computing Department of Computer Sciences Georgia Institute of Technology The University of Texas at Austin Atlanta, GA 30332 Austin, Texas 78712 [email protected] [email protected] Abstract. Combinations of C++ features, like inheritance, templates, and class nesting, allow for the expression of powerful component patterns. In particular, research has demonstrated that, using C++ mixin classes, one can express lay- ered component-based designs concisely with efficient implementations. In this paper, we discuss pragmatic issues related to component-based programming using C++ mixins. We explain surprising interactions of C++ features and poli- cies that sometimes complicate mixin implementations, while other times enable additional functionality without extra effort. 1 Introduction Large software artifacts are arguably among the most complex products of human intellect. The complexity of software has led to implementation methodologies that divide a problem into manageable parts and compose the parts to form the final prod- uct. Several research efforts have argued that C++ templates (a powerful parameteriza- tion mechanism) can be used to perform this division elegantly. In particular, the work of VanHilst and Notkin [29][30][31] showed how one can implement collaboration-based (or role-based) designs using a certain templatized class pattern, known as a mixin class (or just mixin). Compared to other techniques (e.g., a straightforward use of application frameworks [17]) the VanHilst and Notkin method yields less redundancy and reusable components that reflect the structure of the design. At the same time, unnecessary dynamic binding can be eliminated, result- ing into more efficient implementations. -
Comp 411 Principles of Programming Languages Lecture 19 Semantics of OO Languages
Comp 411 Principles of Programming Languages Lecture 19 Semantics of OO Languages Corky Cartwright Mar 10-19, 2021 Overview I • In OO languages, OO data values (except for designated non-OO types) are special records [structures] (finite mappings from names to values). In OO parlance, the components of record are called members. • Some members of an object may be functions called methods. Methods take this (the object in question) as an implicit parameter. Some OO languages like Java also support static methods that do not depend on this; these methods have no implicit parameters. In efficient OO language implementations, method members are shared since they are the same for all instances of a class, but this sharing is an optimization in statically typed OO languages since the collection of methods in a class is immutable during program evaluation (computation). • A method (instance method in Java) can only be invoked on an object (the receiver, an implicit parameter). Additional parameters are optional, depending on whether the method expects them. This invocation process is called dynamic dispatch because the executed code is literally extracted from the object: the code invoked at a call site depends on the value of the receiver, which can change with each execution of the call. • A language with objects is OO if it supports dynamic dispatch (discussed in more detail in Overview II & III) and inheritance, an explicit taxonomy for classifying objects based on their members and class names where superclass/parent methods are inherited unless overridden. • In single inheritance, this taxonomy forms a tree; • In multiple inheritance, it forms a rooted DAG (directed acyclic graph) where the root class is the universal class (Object in Java). -
Multiple Inheritance and the Resolution of Inheritance Conflicts
JOURNAL OF OBJECT TECHNOLOGY Online at http://www.jot.fm. Published by ETH Zurich, Chair of Software Engineering ©JOT, 2005 Vol. 4, No.2, March-April 2005 The Theory of Classification Part 17: Multiple Inheritance and the Resolution of Inheritance Conflicts Anthony J H Simons, Department of Computer Science, University of Sheffield, Regent Court, 211 Portobello Street, Sheffield S1 4DP, UK 1 INTRODUCTION This is the seventeenth article in a regular series on object-oriented theory for non- specialists. Using a second-order λ-calculus model, we have previously modelled the notion of inheritance as a short-hand mechanism for defining subclasses by extending superclass definitions. Initially, we considered the inheritance of type [1] and implementation [2] separately, but later combined both of these in a model of typed inheritance [3]. By simplifying the short-hand inheritance expressions, we showed how these are equivalent to canonical class definitions. We also showed how classes derived by inheritance are type compatible with their superclass. Further aspects of inheritance have included method combination [4], mixin inheritance [5] and inheritance among generic classes [6]. Most recently, we re-examined the ⊕ inheritance operator [7], to show how extending a class definition (the intension of a class) has the effect of restricting the set of objects that belong to the class (the extension of the class). We also added a type constraint to the ⊕ operator, to restrict the types of fields that may legally be combined with a base class to yield a subclass. By varying the form of this constraint, we modelled the typing of inheritance in Java, Eiffel, C++ and Smalltalk. -
OOD and C++ Section 5: Templates
Templates - Generic Programming Decide which algorithms you want: parameterize them so that they work for OOD and C++ a wide-range of types & data structures Section 5: Templates Templates in C++ support generic progarmming Templates provide: •simple way to represent general concepts •simple way to combine concepts Use ‘data-types’ as parameters at compilation Advantage: • more general ‘generic code’ • reuse same code for different data types • less bugs - only implement/debug one piece of code • no overhead on run-time efficiency compared to data specific code Use of Templates Templates in C++ Make the definition of your class as broad as possible! template class declaration: mytemplate.hh template <class DataC> class mytemplate { public: Widely used for container classes: mytemplate(); • Storage independent of data type void function(); • C++ Standard Template Library (Arrays,Lists,…..) protected: int protected_function(); Encapsulation: private: Hide a sophisticated implementation behind a simple double private_data; interface }; template <class DataC>: • specifies a template is being declared • type parameter ‘DataC’ (generic class) will be used • DataC can be any type name: class, built in type, typedef • DataC must contain data/member functions which are used in template implementation. Example - Generic Array Example - Generic Array (cont’d) simple_array.hh template<class DataC> class simple_array { private: #include “simple_array.hh” int size; DataC *array; #include “string.hh” public: void myprogram(){ simple_array(int s); simple_array<int> intarray(100); ~simple_array(); simple_array<double> doublearray(200); DataC& operator[](int i); simple_array<StringC> stringarray(500); }; for (int i(0); i<100; i++) template<class DataC> intarray[i] = i*10; simple_array<DataC>::simple_array(int s) : size(s) { array = new DataC[size]; } // & the rest…. -
Multiple Inheritance
Multiple Inheritance [email protected] Text: Chapter11 and 21, Big C++ Multiple Inheritance • Inheritance discussed so far is Single Inheritance • If a class has only one super class, then it is Single Inheritance • C++, also support Multiple Inheritance , i.e., when a class has more than one parent class Multiple Inheritance • Some of examples are- – Faculty could be Alumnus and Employee in DIICTian scenario – Head-Engineering , needs to be Manager and Engineer both – A CustomerEmployee would be Employee (a Person too), and Customer (a Person too)– forms diamond inheritance Here is how we have multiple inheritance in C++ class C : public A, public B { } • In this case, C inherits from A and B both … “public” Example: Multiple Inheritance Consider Example given • What methods class C has? • What is their visibility in class C? • What data members class C has? • What is their visibility in class C? Example: Multiple Inheritance Issues in Multiple Inheritance : Name ambiguity Base classes A and B of C both has getX() method Issues in Multiple Inheritance : Diamond Inheritance • Class B1 and B2 inherits from L, and • D inherits from B1 and B2, both • Therefore, in D, L inherits twice • It brings in some issues, consider example on next slide • What data and function members C has? • What is their visibility? Issues in Multiple Inheritance : Diamond Inheritance • Class D has – two copies of data ax – Ambiguous method names getX(), getAX() • Two copies of same variable should be more critical Issues in multiple inheritance Virtual Inheritance • C++ addresses this issue by allowing such base (being inherited multiple times) class to be virtual base class • As a result all virtual occurrences of the class throughout the class hierarchy share one actual occurrence of it. -
Julia: a Modern Language for Modern ML
Julia: A modern language for modern ML Dr. Viral Shah and Dr. Simon Byrne www.juliacomputing.com What we do: Modernize Technical Computing Today’s technical computing landscape: • Develop new learning algorithms • Run them in parallel on large datasets • Leverage accelerators like GPUs, Xeon Phis • Embed into intelligent products “Business as usual” will simply not do! General Micro-benchmarks: Julia performs almost as fast as C • 10X faster than Python • 100X faster than R & MATLAB Performance benchmark relative to C. A value of 1 means as fast as C. Lower values are better. A real application: Gillespie simulations in systems biology 745x faster than R • Gillespie simulations are used in the field of drug discovery. • Also used for simulations of epidemiological models to study disease propagation • Julia package (Gillespie.jl) is the state of the art in Gillespie simulations • https://github.com/openjournals/joss- papers/blob/master/joss.00042/10.21105.joss.00042.pdf Implementation Time per simulation (ms) R (GillespieSSA) 894.25 R (handcoded) 1087.94 Rcpp (handcoded) 1.31 Julia (Gillespie.jl) 3.99 Julia (Gillespie.jl, passing object) 1.78 Julia (handcoded) 1.2 Those who convert ideas to products fastest will win Computer Quants develop Scientists prepare algorithms The last 25 years for production (Python, R, SAS, DEPLOY (C++, C#, Java) Matlab) Quants and Computer Compress the Scientists DEPLOY innovation cycle collaborate on one platform - JULIA with Julia Julia offers competitive advantages to its users Julia is poised to become one of the Thank you for Julia. Yo u ' v e k i n d l ed leading tools deployed by developers serious excitement.