Transcript Class Orientation and Introduction

Programming Languages and
Design
Lecture 1
Introduction to Programming Languages
Instructor: Li Ma
Department of Computer Science
Texas Southern University, Houston
January, 2008
Structure of the Lectures
 Review of the last lecture
 Summary of what will be covered
 Main contents
 Summary of what was covered
 Suggestions for the lecture
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Homework
 Goals
Learn programming techniques
Reinforce the lecture material
Evaluate your comprehension
 Exercises and Problems
Understand concepts and put them in practice
 A good preparation for the exams!
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Some Course Goals
 Programming Language Concepts
Learn useful concepts and programming methods
Understand the languages you use, by comparison
Appreciate history, diversity of ideas in programming
Be prepared for new programming methods,
paradigms, tools
 Language design and implementation trade-off
Every convenience has its cost
 Recognize the cost of presenting an abstract view of
machine
 Understand trade-offs in programming language design
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Computer and Programming
 The Computer Age did not really begin until the first
computer was made available to the public in 1951
(Seyed. H. Roosta)
 Modern computers are highly complex system
 Hardware
 Operating System
 Middleware
 Application layers
 Programming a computer is primarily designing and
using abstractions to achieve new goals
 Enormous number of abstractions work together in a highly
organized manner
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Abstractions
 Eliminate detail unnecessary for solving a
particular problem
Complexity is hidden
 Open build upon one another
Allow us to solve increasingly complex problems
 Modern software’s complexity has no precedent
Abstractions are absolutely necessary to manage this
complexity
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Languages as Abstractions
 Human languages are a tool for abstracting
thought
Example: “When I am warm I turn on the fan.”
 A statement communicates a simple intention
 The cognitive and neurological conditions from which the
intention arose are most likely too complex for anyone to
understand
 Meaning of the statement is left to the understanding of the
individual who utters it and ones who hear it
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Languages as Abstractions (cont’)
 Programming Languages
 “Conceptual universe” (Perlis)
 Framework for problem-solving
 A software tool for abstracting computation
 Interface between clients and lower-level facilities
(Implementation)
 Clients are usually humans or their programs
 Lower-level facilities can be files or operating systems
 Example: if (temperature() > 30.0) { turn_on_fan(); }
 A statement involves a complex, but concrete sequence of actions
 Meaning of the statement is fixed by the formal semantics of the
programming language and by the implementations of the functions
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Evolution of Programming
Languages
 Hardware
 Machine code
 Assembly
 Macro Assembly
 FORTRAN 1954
 etc.
Programming in machine code or Assembly is way
too tedious/error-prone
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History of Programming
Languages
 See the poster from O’Reilly
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Why So Many Languages?
 Evolution
From goto to loops, case statements
 Personal Preference
Syntax
Loops vs. recursion
Pointers vs. recursive data types
 Special Purposes
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Application Domains
 Scientific applications (Fortran, TCE)
 Business applications (Cobol)
 Artificial intelligence (LISP)
 Systems programming (C, C++)
 Web service programming (Java, C#)
 Very High-Level Languages (perl)
 Special purpose languages (make, sh)
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What Makes a Language Succeed?
 Expressive Power
 Ease of Use for Novice
 Ease of Implementation
 Open Source
 Availability of Compilers, Libraries
 Economics, Patronage, Inertia
 Syntax that looks like C
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Language Design Issues
 Readability
 Abstractions (functions, classes)
 Orthogonality (no special cases)
 Reliability (type checking)
 Cost (training programmers)
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Why Do We Study Programming
Languages?
 Understand obscure language features
 Choose among ways to express ideas
 Make good use of debuggers, other tools
 Simulate nice features in other languages
 Choose appropriate language for problem
 Learn new languages faster
 Design simple languages
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Computation Models
 A computation model is a formal system that
defines a language and how sentences of the
language are executed by the abstract machine
i.e. how computations are done
 A programming paradigm is a style of
programming a computer
A set of programming techniques and design
principles to write programs in a language
Built on top of a computation model
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Computation Models and Programming
Paradigms
 Declarative Programming
Functional or logic programming
 Procedural/Imperative Programming
 Object-Oriented Programming
 Concurrent Programming
Multiple independent processes (running on the same
CPU or distributed across multiple CPUs/computers)
Communication between processes via
Dataflow
Exchanging messages
Sharing state
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Languages for Programming
Paradigms
 Functional programming
LISP/Scheme, ML, Haskell
 Logic programming
Prolog, SQL, Microsoft Excel
 Imperative programming
Fortran, Pascal, Basic, C
 Object-Oriented programming
Smalltalk, C++, Java, CLOS
 Concurrent programming for real-time systems
Erlang
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Models with Which You Are Already
Familiar
 You already know Java, which supports
Programming with state
(Procedural/Imperative programming)
Object-oriented programming
 It is clear that these two models are important!
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Languages in Common Use
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Compiled by François Labelle from statistics on open-source projects at SourceForge
Questions Worth Discussing for
Programming Languages
 What is the structure (syntax) and meaning (semantics)
of the programming language constructs?
 How does the compiler writer deal with these constructs
in compilation?
 Is the programming language good for the programmer?
 Easy to use?
 Expressive power?
 Readable?
 Easy to detect programming error?
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What’s New in Programming
Languages
 Commercial trend over past 5 years
 Increasing use of type-safe languages: Java, C#, …
 Scripting languages, other languages for web applications
 Teaching trends
 Java replacing C as most common introduction language
 Less emphasis on how data, control represented in machine
 Research and development trends
 Modularity
 Java, C++: standardization of new module features
 Program analysis
 Automated error detection, programming environment, compilation
 Isolation and security
 Sandboxing, language-based security, …
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What’s Worth Studying?
 Dominant languages and paradigms
C, C++, Java
Imperative and Object-oriented languages
 Important implementation ideas
 Performance challenges
Concurrency
 Design tradeoffs
 Concepts that research community is exploring
for new programming languages and tools
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Fundamental Concepts of
Programming Languages
 Variables
declaration, binding, identifier, variable in memory,
scope of a variable
 Identifier, Literals, Expressions
 Data types
integers, floating-point numbers, …
 Data structures
stack, queue, list, …
 Control structures
loops, conditional statements
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Fundamental Concepts of
Programming Languages (cont’)
 Function, procedures and parameter passing
definition, call (application)
 Recursion
For example, inductive definition of a function
 Block structures
 Runtime store organization
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Implementation Methods
 Interpretation (early Lisp)
Source Program
Interpreter
Output
Input
 Compilation (C, ML)
Source
Program
Input
Compiler
Target Program
Target
Program
Output
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Implementation Methods (cont’)
 Hybrid Systems (early Java)
Source
Program
Translator
Intermediate
Program
Intermediate
Program
Virtual Machine
Output
Input
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Overview of Compilation
Character Stream
Scanner (Lexical Analysis)
Token Stream
Parser (Syntax Analysis)
Parse Tree
Semantic Analysis and Intermediate Code Generation
Abstract Syntax Tree or Other
Intermediate Form
Machine-Independent Code Improvement (optional)
Modified Intermediate Form
Target Code Generation
Assembly or Machine Language or
Other Target Language
Machine-Specific Code Improvement (optional)
Modified Target Language
Symbol Table
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