Python and Roles of Variables in Introductory Programming: Experiences from Three Educational Institutions
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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. -
A Feature Model of Actor, Agent, Functional, Object, and Procedural Programming Languages
Accepted Manuscript A feature model of actor, agent, functional, object, and procedural programming languages H.R. Jordan, G. Botterweck, J.H. Noll, A. Butterfield, R.W. Collier PII: S0167-6423(14)00050-1 DOI: 10.1016/j.scico.2014.02.009 Reference: SCICO 1711 To appear in: Science of Computer Programming Received date: 9 March 2013 Revised date: 31 January 2014 Accepted date: 5 February 2014 Please cite this article in press as: H.R. Jordan et al., A feature model of actor, agent, functional, object, and procedural programming languages, Science of Computer Programming (2014), http://dx.doi.org/10.1016/j.scico.2014.02.009 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights • A survey of existing programming language comparisons and comparison techniques. • Definitions of actor, agent, functional, object, and procedural programming concepts. • A feature model of general-purpose programming languages. • Mappings from five languages (C, Erlang, Haskell, Jason, and Java) to this model. A Feature Model of Actor, Agent, Functional, Object, and Procedural Programming Languages H.R. Jordana,∗, G. Botterwecka, J.H. Nolla, A. Butterfieldb, R.W. Collierc aLero, University of Limerick, Ireland bTrinity College Dublin, Dublin 2, Ireland cUniversity College Dublin, Belfield, Dublin 4, Ireland Abstract The number of programming languages is large [1] and steadily increasing [2]. -
Functional Languages
Functional Programming Languages (FPL) 1. Definitions................................................................... 2 2. Applications ................................................................ 2 3. Examples..................................................................... 3 4. FPL Characteristics:.................................................... 3 5. Lambda calculus (LC)................................................. 4 6. Functions in FPLs ....................................................... 7 7. Modern functional languages...................................... 9 8. Scheme overview...................................................... 11 8.1. Get your own Scheme from MIT...................... 11 8.2. General overview.............................................. 11 8.3. Data Typing ...................................................... 12 8.4. Comments ......................................................... 12 8.5. Recursion Instead of Iteration........................... 13 8.6. Evaluation ......................................................... 14 8.7. Storing and using Scheme code ........................ 14 8.8. Variables ........................................................... 15 8.9. Data types.......................................................... 16 8.10. Arithmetic functions ......................................... 17 8.11. Selection functions............................................ 18 8.12. Iteration............................................................. 23 8.13. Defining functions ........................................... -
The Machine That Builds Itself: How the Strengths of Lisp Family
Khomtchouk et al. OPINION NOTE The Machine that Builds Itself: How the Strengths of Lisp Family Languages Facilitate Building Complex and Flexible Bioinformatic Models Bohdan B. Khomtchouk1*, Edmund Weitz2 and Claes Wahlestedt1 *Correspondence: [email protected] Abstract 1Center for Therapeutic Innovation and Department of We address the need for expanding the presence of the Lisp family of Psychiatry and Behavioral programming languages in bioinformatics and computational biology research. Sciences, University of Miami Languages of this family, like Common Lisp, Scheme, or Clojure, facilitate the Miller School of Medicine, 1120 NW 14th ST, Miami, FL, USA creation of powerful and flexible software models that are required for complex 33136 and rapidly evolving domains like biology. We will point out several important key Full list of author information is features that distinguish languages of the Lisp family from other programming available at the end of the article languages and we will explain how these features can aid researchers in becoming more productive and creating better code. We will also show how these features make these languages ideal tools for artificial intelligence and machine learning applications. We will specifically stress the advantages of domain-specific languages (DSL): languages which are specialized to a particular area and thus not only facilitate easier research problem formulation, but also aid in the establishment of standards and best programming practices as applied to the specific research field at hand. DSLs are particularly easy to build in Common Lisp, the most comprehensive Lisp dialect, which is commonly referred to as the “programmable programming language.” We are convinced that Lisp grants programmers unprecedented power to build increasingly sophisticated artificial intelligence systems that may ultimately transform machine learning and AI research in bioinformatics and computational biology. -
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 -
A Formal Component-Based Software Engineering Approach for Developing Trustworthy Systems
A FORMAL COMPONENT-BASED SOFTWARE ENGINEERING APPROACH FOR DEVELOPING TRUSTWORTHY SYSTEMS MUBARAK SAMI MOHAMMAD A THESIS IN THE DEPARTMENT OF COMPUTER SCIENCE AND SOFTWARE ENGINEERING PRESENTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY (COMPUTER SCIENCE) CONCORDIA UNIVERSITY MONTREAL´ ,QUEBEC´ ,CANADA APRIL 2009 °c MUBARAK SAMI MOHAMMAD, 2009 CONCORDIA UNIVERSITY School of Graduate Studies This is to certify that the thesis prepared By: Mr. Mubarak Sami Mohammad Entitled: A Formal Component-Based Software Engineering Approach for Developing Trustworthy Systems and submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Computer Science) (Computer Science) complies with the regulations of this University and meets the accepted standards with re- spect to originality and quality. Signed by the final examining committee: Chair Dr. External Examiner Dr. Nicholas Graham External to Program Dr. Jamal Bentahar Examiner Dr. Joey Paquet Examiner Dr. Juergen Rilling Supervisor Dr. Vasu Alagar Co-supervisor Dr. Olga Ormandjieva Approved Chair of Department or Graduate Program Director 20 Dr. Robin A.L. Drew, Dean Faculty of Engineering and Computer Science Abstract A Formal Component-Based Software Engineering Approach for Developing Trustworthy Systems Mubarak Sami Mohammad, Ph.D. Concordia University, 2009 Software systems are increasingly becoming ubiquitous, affecting the way we experience the world. Embedded software systems, especially those used in smart devices, have be- come an essential constituent of the technological infrastructure of modern societies. Such systems, in order to be trusted in society, must be proved to be trustworthy. Trustworthiness is a composite non-functional property that implies safety, timeliness, security, availability, and reliability. -
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 . -
(Pdf) of from Push/Enter to Eval/Apply by Program Transformation
From Push/Enter to Eval/Apply by Program Transformation MaciejPir´og JeremyGibbons Department of Computer Science University of Oxford [email protected] [email protected] Push/enter and eval/apply are two calling conventions used in implementations of functional lan- guages. In this paper, we explore the following observation: when considering functions with mul- tiple arguments, the stack under the push/enter and eval/apply conventions behaves similarly to two particular implementations of the list datatype: the regular cons-list and a form of lists with lazy concatenation respectively. Along the lines of Danvy et al.’s functional correspondence between def- initional interpreters and abstract machines, we use this observation to transform an abstract machine that implements push/enter into an abstract machine that implements eval/apply. We show that our method is flexible enough to transform the push/enter Spineless Tagless G-machine (which is the semantic core of the GHC Haskell compiler) into its eval/apply variant. 1 Introduction There are two standard calling conventions used to efficiently compile curried multi-argument functions in higher-order languages: push/enter (PE) and eval/apply (EA). With the PE convention, the caller pushes the arguments on the stack, and jumps to the function body. It is the responsibility of the function to find its arguments, when they are needed, on the stack. With the EA convention, the caller first evaluates the function to a normal form, from which it can read the number and kinds of arguments the function expects, and then it calls the function body with the right arguments. -
Programming Languages
Lecture 19 / Chapter 13 COSC1300/ITSC 1401/BCIS 1405 12/5/2004 Lecture 19 / Chapter 13 COSC1300/ITSC 1401/BCIS 1405 12/5/2004 General Items: Computer Program • Lab? Ok? • Read the extra credits • A series of instructions that direct a computer to perform tasks • Need to come to class • Such as? Who is the programmer? • Have a quiz / no books / use notes -> What is the big idea • School is almost over • Programming language is a series of rules for writing the instructions • • There are hundreds of computer programs – need-based! Reading Materials: Programming language • - Two basic types: Low- and high-level programming languages Miscellaneous: o Low-level: Programming language that is machine-dependent ° Must be run on specific machines o High-level: Language that is machine-independent ° Can be run on different types of machines Programming Language Low Level High Level Machine Assembly Language Language Procedural Nonprocedural Remember: Ultimately, everything must be converted to the machine language! Object Oriented F.Farahmand 1 / 12 File: lec14chap13f04.doc F.Farahmand 2 / 12 File: lec14chap13f04.doc Lecture 19 / Chapter 13 COSC1300/ITSC 1401/BCIS 1405 12/5/2004 Lecture 19 / Chapter 13 COSC1300/ITSC 1401/BCIS 1405 12/5/2004 Categories of programming languages o Nonprocedural language -> Programmer specifies only what the - Machine language program should accomplish; it does not explain how o Only language computer understands directly - Forth-generation language - Assembly language o Syntax is closer to human language than that -
Making a Faster Curry with Extensional Types
Making a Faster Curry with Extensional Types Paul Downen Simon Peyton Jones Zachary Sullivan Microsoft Research Zena M. Ariola Cambridge, UK University of Oregon [email protected] Eugene, Oregon, USA [email protected] [email protected] [email protected] Abstract 1 Introduction Curried functions apparently take one argument at a time, Consider these two function definitions: which is slow. So optimizing compilers for higher-order lan- guages invariably have some mechanism for working around f1 = λx: let z = h x x in λy:e y z currying by passing several arguments at once, as many as f = λx:λy: let z = h x x in e y z the function can handle, which is known as its arity. But 2 such mechanisms are often ad-hoc, and do not work at all in higher-order functions. We show how extensional, call- It is highly desirable for an optimizing compiler to η ex- by-name functions have the correct behavior for directly pand f1 into f2. The function f1 takes only a single argu- expressing the arity of curried functions. And these exten- ment before returning a heap-allocated function closure; sional functions can stand side-by-side with functions native then that closure must subsequently be called by passing the to practical programming languages, which do not use call- second argument. In contrast, f2 can take both arguments by-name evaluation. Integrating call-by-name with other at once, without constructing an intermediate closure, and evaluation strategies in the same intermediate language ex- this can make a huge difference to run-time performance in presses the arity of a function in its type and gives a princi- practice [Marlow and Peyton Jones 2004].