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CS 424/524
PROGRAMMING LANGUAGES
Overview
Copyright © 2006 The McGraw-Hill Companies, Inc.
Course Organization
Class meetings:
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MW 3:55pm-5:15pm
TH N326
Instructor Information:
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Dr. Mary Ellen Weisskopf
Email: [email protected]
Phone number: (256) 824-6306
Office: Technology Hall, N300A
Copyright © 2006 The McGraw-Hill Companies, Inc.
Programming Languages
2nd edition
Tucker and Noonan
Chapter 1
Overview
A good programming language is a
conceptual universe for thinking about
programming.
A. Perlis
Copyright © 2006 The McGraw-Hill Companies, Inc.
Contents
1.1
1.2
1.3
1.4
1.5
Principles
Paradigms
Special Topics
A Brief History
On Language Design
1.5.1 Design Constraints
1.5.2 Outcomes and Goals
1.6 Compilers, Interpreters, and Virtual
Machines
Copyright © 2006 The McGraw-Hill Companies, Inc.
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
Copyright © 2006 The McGraw-Hill Companies, Inc.
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?
Copyright © 2006 The McGraw-Hill Companies, Inc.
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
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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
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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
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1.2 Paradigms
A programming paradigm is a pattern of problemsolving thought that underlies a particular category of
programs and languages.
There are four main programming paradigms:
– Imperative
– Object-oriented
– Functional
– Logic (declarative)
Copyright © 2006 The McGraw-Hill Companies, Inc.
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, …
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The von Neumann-Eckert Model
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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
Copyright © 2006 The McGraw-Hill Companies, Inc.
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, …
Copyright © 2006 The McGraw-Hill Companies, Inc.
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
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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?
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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
Copyright © 2006 The McGraw-Hill Companies, Inc.
Copyright © 2006 The McGraw-Hill Companies, Inc.
1.5 On Language Design
1.5.1 – Design Constraints
– Computer architecture
– Technical setting
– Standards
– Legacy systems
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Design Outcomes and Goals
• Outcome: What makes a language successful?
• Goals: What characteristics make a programming
language “good” ?
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What Makes A Successful Language?
Key characteristics:
– Simplicity and readability
– Clarity about binding
– Reliability
– Support
– Abstraction
– Orthogonality
– Efficient implementation
Copyright © 2006 The McGraw-Hill Companies, Inc.
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.
Copyright © 2006 The McGraw-Hill Companies, Inc.
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
Copyright © 2006 The McGraw-Hill Companies, Inc.
Major Binding Times
• Major binding times
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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
Copyright © 2006 The McGraw-Hill Companies, Inc.
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
Copyright © 2006 The McGraw-Hill Companies, Inc.
Language Support
• Accessible (public domain) compilers/interpreters
• Good texts and tutorials
• Wide community of users
• Integrated with development environments (IDEs)
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Abstraction in Programming
• Data
– Programmer-defined types/classes
– Class libraries
• Procedural
– Programmer-defined functions
– Standard function libraries
• Supports code reuse; simplifies the programming
process.
Copyright © 2006 The McGraw-Hill Companies, Inc.
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).
Copyright © 2006 The McGraw-Hill Companies, Inc.
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
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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
Copyright © 2006 The McGraw-Hill Companies, Inc.
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)
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Translation/Execution – Compiler
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Compilers
• Translates source code program into object code
(machine language).
• Object code can be executed without repeating
the compilation process.
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Translation/Execution – Interpreter
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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.
Copyright © 2006 The McGraw-Hill Companies, Inc.
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
Copyright © 2006 The McGraw-Hill Companies, Inc.
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
Copyright © 2006 The McGraw-Hill Companies, Inc.