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CS 424/524 PROGRAMMING LANGUAGES

CS 424/524 PROGRAMMING LANGUAGES. Overview. Course Organization. Class meetings: MW 3:55pm-5:15pm TH N326 Instructor Information: Dr. Mary Ellen Weisskopf Email: weisskop@cs.uah.edu Phone number: (256) 824-6306 Office: Technology Hall, N300A.

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CS 424/524 PROGRAMMING LANGUAGES

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  1. CS 424/524PROGRAMMING LANGUAGES Overview

  2. Course Organization Class meetings: • MW 3:55pm-5:15pm • TH N326 Instructor Information: • Dr. Mary Ellen Weisskopf • Email: weisskop@cs.uah.edu • Phone number: (256) 824-6306 • Office: Technology Hall, N300A

  3. Programming Languages2nd editionTucker and Noonan Chapter 1 Overview A good programming language is a conceptual universe for thinking about programming. A. Perlis

  4. Contents 1.1 Principles 1.2 Paradigms 1.3 Special Topics 1.4 A Brief History 1.5 On Language Design 1.5.1 Design Constraints 1.5.2 Outcomes and Goals 1.6 Compilers, Interpreters, and Virtual Machines

  5. 1.1 Principles Programming languages have several properties: • Syntax • Names & types • Semantics For any language: • Its designers must define these properties • Its programmers must master these properties

  6. Syntax • The syntax of a programming language is a precise description of all its grammatically correct programs. • Rules that define how symbols can be combined to create legal language statements and how the statements can be combined into programs When studying syntax, we ask questions like: • What is the grammar for the language? (grammar = rules) • What is the basic vocabulary? (operators/variables/…) • How are syntax errors detected?

  7. Names Various kinds of entities in a program have names: variables, functions, parameters, classes, objects, … Named entities are bound in a running program to: • Scope • Visibility • Type • Lifetime

  8. Types A type is a collection of values and a collection of operations on those values. • Simple types: numbers, characters, booleans, … • Structured types: Strings, lists, trees, hash tables, … A language’s type system can help to: • Determine legal operations • Detect type errors

  9. Semantics A program’s meaning is called its semantics. In studying semantics, we ask questions like: • When a program is running, what happens to the values of the variables? • What does each statement mean? • What underlying model governs the run-time behavior of a function call? • How are objects allocated to memory at run-time? • Run-time stack, heap • Related to function behavior & implementation

  10. 1.2 Paradigms A programming paradigm is a pattern of problem-solving thought that underlies a particular category of programs and languages. There are four main programming paradigms: • Imperative • Object-oriented • Functional • Logic (declarative)

  11. Imperative Paradigm Follows the classic von Neumann-Eckert model: • Program and data are indistinguishable in memory • Program = a sequence of commands • State = values of all variables when program runs Large programs use procedural abstraction as a way to organize individual imperative statements. Example imperative languages: • Cobol, Fortran, C, Ada, Perl, …

  12. The von Neumann-Eckert Model

  13. Object-oriented (OO) Paradigm An OO Program consists of a collection of objects that interact by passing messages that transform object state. OO languages are characterized by • Data encapsulation/abstraction • Inheritance • Polymorphism Example OO languages: Smalltalk, Java, C++, C#, and Python

  14. Functional Paradigm Functional programming models a computation as a collection of mathematical functions. • Input = domain • Output = range Functional languages are characterized by: • Functional composition • Recursion Example functional languages: • Lisp, Scheme, ML, Haskell, …

  15. Logic Paradigm (Declarative) Logic programming declares what outcome the program should accomplish, rather than how it should be accomplished. Rule-based When studying logic programming we see: • Programs as sets of constraints on a problem • Programs that achieve all possible solutions • Programs that are “nondeterministic” Example logic programming languages: Prolog

  16. 1.3 Special Topics • Event handling • E.g., GUIs, home security systems, monitoring systems of various kinds • Concurrency • e.g., Client-server programs, rendering (in computer graphics) • Correctness • How can we prove that a program does what it is supposed to do under all circumstances?

  17. 1.4 A Brief History How and when did programming languages evolve? What communities have developed and used them? • Artificial Intelligence • Computer Science Education • Science and Engineering • Information Systems • Systems and Networks • World Wide Web

  18. 1.5 On Language Design 1.5.1 – Design Constraints • Computer architecture • Technical setting • Standards • Legacy systems

  19. Design Outcomes and Goals • Outcome: What makes a language successful? • Goals: What characteristics make a programming language “good” ?

  20. What Makes A Successful Language? Key characteristics: • Simplicity and readability • Clarity about binding • Reliability • Support • Abstraction • Orthogonality • Efficient implementation

  21. Simplicity (Writeability) and Readability • Easy to learn • Don’t include too many features • Don’t make it too complex • Simple conceptual model of semantics. • Uniformity:Similar syntax => similar semantics. • Lexical and syntactic conventions: • descriptive identifier names, blocking of compound statements • Readability is not synonymous with wordiness: COBOL is not easier to read.

  22. Clarity about Binding A language element is bound to a property at the time that property is defined for it. So a binding is the association between an object and a property of that object • Examples: • a variable and its type • a variable and its value

  23. Major Binding Times • Major binding times • Language definition time • Language implementation time • Program writing time • Compile time • Load time • Execution time • In general, • Early binding takes place at compile-time or before • Late binding takes place at load time or run time

  24. Reliability A language is reliable if: • Program behavior is the same on different platforms • E.g., early versions of Fortran weren’t • Type errors are detected • E.g., C vs Haskell • Semantic errors are properly trapped • E.g., C vs C++ or Java • Memory leaks are prevented • E.g., C vs Java

  25. Language Support • Accessible (public domain) compilers/interpreters • Good texts and tutorials • Wide community of users • Integrated with development environments (IDEs)

  26. Abstraction in Programming • Data • Programmer-defined types/classes • Class libraries • Procedural • Programmer-defined functions • Standard function libraries • Supports code reuse; simplifies the programming process.

  27. Orthogonality • A language is orthogonal if it is built on a small, mutually independent set of primitive operations. • You can use one feature without worrying about how it affects others • Rules don’t have exceptions; • Context doesn’t affect the behavior of a language feature (e.g., a reserved word doesn’t have different meanings based on where it’s used).

  28. Orthogonality • Non-orthogonal examples (rule exceptions): • In C, a function can return a struct, but not an array. Orthogonality => return value can be from any type. • Most imperative languages don’t allow function definitions to be passed as arguments • Orthogonal languages are in general easier to read and write

  29. Efficient Implementation • Embedded systems • Real-time responsiveness (e.g., navigation) • Failures of early Ada implementations – real time? • Web applications • Responsiveness to users (e.g., Google search) • Corporate database applications • Efficient search and updating

  30. 1.6 Compilers and Virtual Machines Compiler – produces machine code Interpreter – executes instructions directly • Example compiled languages: • Fortran, Cobol, C, C++ • Example interpreted languages: • Scheme, Haskell, Python • Hybrid compilation/interpretation: Java • The Java Virtual Machine (JVM)

  31. Translation/Execution – Compiler

  32. Compilers • Translates source code program into object code (machine language). • Object code can be executed without repeating the compilation process.

  33. Translation/Execution – Interpreter

  34. Interpreters • No object code. • Interpretive routines • Examine the program. When an action is recognized, do it (by calling one of the routines). • Slower execution (than with compilation) • Better interactive development environment.

  35. Virtual Machines & Interpreters • Some languages (e.g., Java) are compiled to machine code (byte code) that runs on a virtual machine (the JVM). • To run Java programs install a JVM for your machine that interprets the byte code. • Benefit: compiler is platform independent • Disdadvantage: slow • Solution: JIT compilers

  36. Summary SUMMARY • Programming language principles • Grammars, syntax, semantics • What makes a language successful? • Reliable, readable, writeable • Supported by simplicity, orthogonality, efficiency, support, … • Paradigms • Imperative, object-oriented, functional, logic • Language implementation • Compilers & interpreters

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