A New Paradigm for Component-Based Development Johan G
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Programming Paradigms & Object-Oriented
4.3 (Programming Paradigms & Object-Oriented- Computer Science 9608 Programming) with Majid Tahir Syllabus Content: 4.3.1 Programming paradigms Show understanding of what is meant by a programming paradigm Show understanding of the characteristics of a number of programming paradigms (low- level, imperative (procedural), object-oriented, declarative) – low-level programming Demonstrate an ability to write low-level code that uses various address modes: o immediate, direct, indirect, indexed and relative (see Section 1.4.3 and Section 3.6.2) o imperative programming- see details in Section 2.3 (procedural programming) Object-oriented programming (OOP) o demonstrate an ability to solve a problem by designing appropriate classes o demonstrate an ability to write code that demonstrates the use of classes, inheritance, polymorphism and containment (aggregation) declarative programming o demonstrate an ability to solve a problem by writing appropriate facts and rules based on supplied information o demonstrate an ability to write code that can satisfy a goal using facts and rules Programming paradigms 1 4.3 (Programming Paradigms & Object-Oriented- Computer Science 9608 Programming) with Majid Tahir Programming paradigm: A programming paradigm is a set of programming concepts and is a fundamental style of programming. Each paradigm will support a different way of thinking and problem solving. Paradigms are supported by programming language features. Some programming languages support more than one paradigm. There are many different paradigms, not all mutually exclusive. Here are just a few different paradigms. Low-level programming paradigm The features of Low-level programming languages give us the ability to manipulate the contents of memory addresses and registers directly and exploit the architecture of a given processor. -
15-150 Lectures 27 and 28: Imperative Programming
15-150 Lectures 27 and 28: Imperative Programming Lectures by Dan Licata April 24 and 26, 2012 In these lectures, we will show that imperative programming is a special case of functional programming. We will also investigate the relationship between imperative programming and par- allelism, and think about what happens when the two are combined. 1 Mutable Cells Functional programming is all about transforming data into new data. Imperative programming is all about updating and changing data. To get started with imperative programming, ML provides a type 'a ref representing mutable memory cells. This type is equipped with: • A constructor ref : 'a -> 'a ref. Evaluating ref v creates and returns a new memory cell containing the value v. Pattern-matching a cell with the pattern ref p gets the contents. • An operation := : 'a ref * 'a -> unit that updates the contents of a cell. For example: val account = ref 100 val (ref cur) = account val () = account := 400 val (ref cur) = account 1. In the first line, evaluating ref 100 creates a new memory cell containing the value 100, which we will write as a box: 100 and binds the variable account to this cell. 2. The next line pattern-matches account as ref cur, which binds cur to the value currently in the box, in this case 100. 3. The next line updates the box to contain the value 400. 4. The final line reads the current value in the box, in this case 400. 1 It's important to note that, with mutation, the same program can have different results if it is evaluated multiple times. -
Functional and Imperative Object-Oriented Programming in Theory and Practice
Uppsala universitet Inst. för informatik och media Functional and Imperative Object-Oriented Programming in Theory and Practice A Study of Online Discussions in the Programming Community Per Jernlund & Martin Stenberg Kurs: Examensarbete Nivå: C Termin: VT-19 Datum: 14-06-2019 Abstract Functional programming (FP) has progressively become more prevalent and techniques from the FP paradigm has been implemented in many different Imperative object-oriented programming (OOP) languages. However, there is no indication that OOP is going out of style. Nevertheless the increased popularity in FP has sparked new discussions across the Internet between the FP and OOP communities regarding a multitude of related aspects. These discussions could provide insights into the questions and challenges faced by programmers today. This thesis investigates these online discussions in a small and contemporary scale in order to identify the most discussed aspect of FP and OOP. Once identified the statements and claims made by various discussion participants were selected and compared to literature relating to the aspects and the theory behind the paradigms in order to determine whether there was any discrepancies between practitioners and theory. It was done in order to investigate whether the practitioners had different ideas in the form of best practices that could influence theories. The most discussed aspect within FP and OOP was immutability and state relating primarily to the aspects of concurrency and performance. This thesis presents a selection of representative quotes that illustrate the different points of view held by groups in the community and then addresses those claims by investigating what is said in literature. -
Unit 2 — Imperative and Object-Oriented Paradigm
Programming Paradigms Unit 2 — Imperative and Object-oriented Paradigm J. Gamper Free University of Bozen-Bolzano Faculty of Computer Science IDSE PP 2018/19 Unit 2 – Imperative and Object-oriented Paradigm 1/38 Outline 1 Imperative Programming Paradigm 2 Abstract Data Types 3 Object-oriented Approach PP 2018/19 Unit 2 – Imperative and Object-oriented Paradigm 2/38 Imperative Programming Paradigm Outline 1 Imperative Programming Paradigm 2 Abstract Data Types 3 Object-oriented Approach PP 2018/19 Unit 2 – Imperative and Object-oriented Paradigm 3/38 Imperative Programming Paradigm Imperative Paradigm/1 The imperative paradigm is the oldest and most popular paradigm Based on the von Neumann architecture of computers Imperative programs define sequences of commands/statements for the computer that change a program state (i.e., set of variables) Commands are stored in memory and executed in the order found Commands retrieve data, perform a computation, and assign the result to a memory location Data ←→ Memory CPU (Data and Address Program) ←→ The hardware implementation of almost all Machine code computers is imperative 8B542408 83FA0077 06B80000 Machine code, which is native to the C9010000 008D0419 83FA0376 computer, and written in the imperative style B84AEBF1 5BC3 PP 2018/19 Unit 2 – Imperative and Object-oriented Paradigm 4/38 Imperative Programming Paradigm Imperative Paradigm/2 Central elements of imperative paradigm: Assigment statement: assigns values to memory locations and changes the current state of a program Variables refer -
Reasoning with Ambiguity
Noname manuscript No. (will be inserted by the editor) Reasoning with Ambiguity Christian Wurm the date of receipt and acceptance should be inserted later Abstract We treat the problem of reasoning with ambiguous propositions. Even though ambiguity is obviously problematic for reasoning, it is no less ob- vious that ambiguous propositions entail other propositions (both ambiguous and unambiguous), and are entailed by other propositions. This article gives a formal analysis of the underlying mechanisms, both from an algebraic and a logical point of view. The main result can be summarized as follows: sound (and complete) reasoning with ambiguity requires a distinction between equivalence on the one and congruence on the other side: the fact that α entails β does not imply β can be substituted for α in all contexts preserving truth. With- out this distinction, we will always run into paradoxical results. We present the (cut-free) sequent calculus ALcf , which we conjecture implements sound and complete propositional reasoning with ambiguity, and provide it with a language-theoretic semantics, where letters represent unambiguous meanings and concatenation represents ambiguity. Keywords Ambiguity, reasoning, proof theory, algebra, Computational Linguistics Acknowledgements Thanks to Timm Lichte, Roland Eibers, Roussanka Loukanova and Gerhard Schurz for helpful discussions; also I want to thank two anonymous reviewers for their many excellent remarks! 1 Introduction This article gives an extensive treatment of reasoning with ambiguity, more precisely with ambiguous propositions. We approach the problem from an Universit¨atD¨usseldorf,Germany Universit¨atsstr.1 40225 D¨usseldorf Germany E-mail: [email protected] 2 Christian Wurm algebraic and a logical perspective and show some interesting surprising results on both ends, which lead up to some interesting philosophical questions, which we address in a preliminary fashion. -
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 . -
A Computer-Verified Monadic Functional Implementation of the Integral
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Theoretical Computer Science 411 (2010) 3386–3402 Contents lists available at ScienceDirect Theoretical Computer Science journal homepage: www.elsevier.com/locate/tcs A computer-verified monadic functional implementation of the integral Russell O'Connor, Bas Spitters ∗ Radboud University Nijmegen, Netherlands article info a b s t r a c t Article history: We provide a computer-verified exact monadic functional implementation of the Riemann Received 10 September 2008 integral in type theory. Together with previous work by O'Connor, this may be seen as Received in revised form 13 January 2010 the beginning of the realization of Bishop's vision to use constructive mathematics as a Accepted 23 May 2010 programming language for exact analysis. Communicated by G.D. Plotkin ' 2010 Elsevier B.V. All rights reserved. Keywords: Type theory Functional programming Exact real analysis Monads 1. Introduction Integration is one of the fundamental techniques in numerical computation. However, its implementation using floating- point numbers requires continuous effort on the part of the user in order to ensure that the results are correct. This burden can be shifted away from the end-user by providing a library of exact analysis in which the computer handles the error estimates. For high assurance we use computer-verified proofs that the implementation is actually correct; see [21] for an overview. It has long been suggested that, by using constructive mathematics, exact analysis and provable correctness can be unified [7,8]. Constructive mathematics provides a high-level framework for specifying computations (Section 2.1). -
Edinburgh Research Explorer
Edinburgh Research Explorer Propositions as Types Citation for published version: Wadler, P 2015, 'Propositions as Types', Communications of the ACM, vol. 58, no. 12, pp. 75-84. https://doi.org/10.1145/2699407 Digital Object Identifier (DOI): 10.1145/2699407 Link: Link to publication record in Edinburgh Research Explorer Document Version: Peer reviewed version Published In: Communications of the ACM General rights Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please contact [email protected] providing details, and we will remove access to the work immediately and investigate your claim. Download date: 28. Sep. 2021 Propositions as Types ∗ Philip Wadler University of Edinburgh [email protected] 1. Introduction cluding Agda, Automath, Coq, Epigram, F#,F?, Haskell, LF, ML, Powerful insights arise from linking two fields of study previously NuPRL, Scala, Singularity, and Trellys. thought separate. Examples include Descartes’s coordinates, which Propositions as Types is a notion with mystery. Why should it links geometry to algebra, Planck’s Quantum Theory, which links be the case that intuitionistic natural deduction, as developed by particles to waves, and Shannon’s Information Theory, which links Gentzen in the 1930s, and simply-typed lambda calculus, as devel- thermodynamics to communication. -
A Multi-Modal Analysis of Anaphora and Ellipsis
University of Pennsylvania Working Papers in Linguistics Volume 5 Issue 2 Current Work in Linguistics Article 2 1998 A Multi-Modal Analysis of Anaphora and Ellipsis Gerhard Jaeger Follow this and additional works at: https://repository.upenn.edu/pwpl Recommended Citation Jaeger, Gerhard (1998) "A Multi-Modal Analysis of Anaphora and Ellipsis," University of Pennsylvania Working Papers in Linguistics: Vol. 5 : Iss. 2 , Article 2. Available at: https://repository.upenn.edu/pwpl/vol5/iss2/2 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/pwpl/vol5/iss2/2 For more information, please contact [email protected]. A Multi-Modal Analysis of Anaphora and Ellipsis This working paper is available in University of Pennsylvania Working Papers in Linguistics: https://repository.upenn.edu/pwpl/vol5/iss2/2 A Multi-Modal Analysis of Anaphora and Ellipsis Gerhard J¨ager 1. Introduction The aim of the present paper is to outline a unified account of anaphora and ellipsis phenomena within the framework of Type Logical Categorial Gram- mar.1 There is at least one conceptual and one empirical reason to pursue such a goal. Firstly, both phenomena are characterized by the fact that they re-use semantic resources that are also used elsewhere. This issue is discussed in detail in section 2. Secondly, they show a striking similarity in displaying the characteristic ambiguity between strict and sloppy readings. This supports the assumption that in fact the same mechanisms are at work in both cases. (1) a. John washed his car, and Bill did, too. b. John washed his car, and Bill waxed it. -
Topics in Philosophical Logic
Topics in Philosophical Logic The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Litland, Jon. 2012. Topics in Philosophical Logic. Doctoral dissertation, Harvard University. Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:9527318 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA © Jon Litland All rights reserved. Warren Goldfarb Jon Litland Topics in Philosophical Logic Abstract In “Proof-Theoretic Justification of Logic”, building on work by Dummett and Prawitz, I show how to construct use-based meaning-theories for the logical constants. The assertability-conditional meaning-theory takes the meaning of the logical constants to be given by their introduction rules; the consequence-conditional meaning-theory takes the meaning of the log- ical constants to be given by their elimination rules. I then consider the question: given a set of introduction (elimination) rules , what are the R strongest elimination (introduction) rules that are validated by an assertabil- ity (consequence) conditional meaning-theory based on ? I prove that the R intuitionistic introduction (elimination) rules are the strongest rules that are validated by the intuitionistic elimination (introduction) rules. I then prove that intuitionistic logic is the strongest logic that can be given either an assertability-conditional or consequence-conditional meaning-theory. In “Grounding Grounding” I discuss the notion of grounding. My discus- sion revolves around the problem of iterated grounding-claims. -
Functional Programming Functional Vs. Imperative Referential
Functional vs. Imperative Referential transparency Imperative programming concerned with “how.” The main (good) property of functional programming is Functional Programming referential transparency. Functional programming concerned with “what.” COMS W4115 Every expression denotes a single value. Based on the mathematics of the lambda calculus Prof. Stephen A. Edwards (Church as opposed to Turing). The value cannot be changed by evaluating an expression Spring 2003 or by sharing it between different parts of the program. “Programming without variables” Columbia University No references to global data; there is no global data. Department of Computer Science It is inherently concise, elegant, and difficult to create subtle bugs in. There are no side-effects, unlike in referentially opaque Original version by Prof. Simon Parsons languages. It’s a cult: once you catch the functional bug, you never escape. The Joy of Pascal Strange behavior Variables program example(output) This prints 5 then 4. At the heart of the “problem” is fact that the global data var flag: boolean; flag affects the value of f. Odd since you expect function f(n:int): int In particular begin f (1) + f (2) = f (2) + f (1) if flag then f := n flag := not flag else f := 2*n; gives the offending behavior flag := not flag Mathematical functions only depend on their inputs end They have no memory Eliminating assignments eliminates such problems. begin In functional languages, variables not names for storage. flag := true; What does this print? Instead, they’re names that refer to particular values. writeln(f(1) + f(2)); writeln(f(2) + f(1)); Think of them as not very variables. -
Imperative Programming Languages (IPL)
Imperative Programming Languages (IPL) © Definitions: • The imperative (or procedural) paradigm is the closest to the structure of actual computers. • It is a model that is based on moving bits around and changing machine state • Programming languages based on the imperative paradigm have the following characteristics: The basic unit of abstraction is the PROCEDURE, whose basic structure is a sequence of statements that are executed in succession, abstracting the way that the program counter is incremented so as to proceed through a series of machine instructions residing in sequential hardware memory cells. The sequential flow of execution can be modified by conditional and looping statements (as well as by the very low-level goto statement found in many imperative languages), which abstract the conditional and unconditional branch instructions found in the underlying machine instruction set. Variables play a key role, and serve as abstractions of hardware memory cells. Typically, a given variable may assume many different values of the course of the execution of a program, just as a hardware memory cell may contain many different values. Thus, the assignment statement is a very important and frequently used statement. © Examples of imperative languages: • FORTRAN, Algol, COBOL, Pascal, C (and to some extent C++), BASIC, Ada - and many more. © PL/I • PL/I (1963-5): was one of the few languages that attempted to be a general purpose language, rather than aiming at a particular category of programming. • PL/I incorporated a blend of features from FORTRAN, ALGOL, and COBOL, plus allowed programmers to create concurrent tasks, handle run-time exceptions, use recursive procedures, and use pointers.