brief intro for Programming Languages 02-chapter-1

CS 415: Programming
CS 415: Programming
Languages
Languages
Chapter 1
Chapter 1
Aaron Bloomfield
Aaron Bloomfield
Fall 2005
Fall 2005

The first computers
The first computers
Scales – computed relative weight of two items
Scales – computed relative weight of two items

Computed if the first item’s weight was less than, equal to, or
Computed if the first item’s weight was less than, equal to, or
greater than the second item’s weight
greater than the second item’s weight
Abacus – performed mathematical computations
Abacus – performed mathematical computations

Primarily thought of as Chinese, but also Japanese, Mayan,
Primarily thought of as Chinese, but also Japanese, Mayan,
Russian, and Roman versions
Russian, and Roman versions

Can do square roots and cube roots
Can do square roots and cube roots

Computer Size
Computer Size
ENIAC then…
ENIAC today…
With computers (small) size does matter!
With computers (small) size does matter!

Why study programming
languages?
languages?
Become a better software engineer
Become a better software engineer

Understand how to use language features
Understand how to use language features

Appreciate implementation issues
Appreciate implementation issues
Better background for language selection
Better background for language selection

Familiar with range of languages
Familiar with range of languages

Understand issues / advantages / disadvantages
Understand issues / advantages / disadvantages
Better able to learn languages
Better able to learn languages

You might need to know a lot
You might need to know a lot

languages?
languages?
Better understanding of implementation issues
Better understanding of implementation issues

How is “this feature” implemented?
How is “this feature” implemented?

Why does “this part” run so slowly?
Why does “this part” run so slowly?
Better able to design languages
Better able to design languages

Those who ignore history are bound to repeat it…
Those who ignore history are bound to repeat it…

Why are there so many
programming languages?
There are thousands!
There are thousands!
Evolution
Evolution

Structured languages -> OO programming
Structured languages -> OO programming
Special purposes
Special purposes

Lisp for symbols; Snobol for strings; C for systems;
Lisp for symbols; Snobol for strings; C for systems;
Prolog for relationships
Prolog for relationships
Personal preference
Personal preference

Programmers have their own personal tastes
Programmers have their own personal tastes
Expressive power
Expressive power

Some features allow you to express your ideas better
Some features allow you to express your ideas better

Easy to use
Easy to use

Especially for teaching / learning tasks
Especially for teaching / learning tasks
Ease of implementation
Ease of implementation

Easy to write a compiler / interpreter for
Easy to write a compiler / interpreter for
Good compilers
Good compilers

Fortran in the 50’s and 60’s
Fortran in the 50’s and 60’s
Economics, patronage
Economics, patronage

Cobol and Ada, for example
Cobol and Ada, for example

Programming domains
Programming domains
Scientific applications
Scientific applications

Using the computer as a large calculator
Using the computer as a large calculator

Fortran and friends, some Algol, APL
Fortran and friends, some Algol, APL

Using the computer for symbol manipulation
Using the computer for symbol manipulation

Mathematica
Mathematica
Business applications
Business applications

Data processing and business procedures
Data processing and business procedures

Cobol, some PL/1, RPG, spreadsheets
Cobol, some PL/1, RPG, spreadsheets
Systems programming
Systems programming

Building operating systems and utilities
Building operating systems and utilities

C, PL/S, ESPOL, Bliss, some Algol and derivitaves
C, PL/S, ESPOL, Bliss, some Algol and derivitaves

Programming domains
Programming domains
Parallel programming
Parallel programming

Parallel and distributed systems
Parallel and distributed systems

Ada, CSP, Modula, DP, Mentat/Legion
Ada, CSP, Modula, DP, Mentat/Legion
Artificial intelligence
Artificial intelligence

Uses symbolic rather than numeric computations
Uses symbolic rather than numeric computations

Lists as main data structure
Lists as main data structure

Flexibility (code = data)
Flexibility (code = data)

Lisp in 1959, Prolog in the 1970s
Lisp in 1959, Prolog in the 1970s
Scripting languages
Scripting languages

A list of commands to be executed
A list of commands to be executed

UNIX shell programming, awk, tcl, Perl
UNIX shell programming, awk, tcl, Perl

Programming domains
Programming domains
Education
Education

Languages designed to facilitate teaching
Languages designed to facilitate teaching

Pascal, BASIC, Logo
Pascal, BASIC, Logo
Special purpose
Special purpose

Other than the above…
Other than the above…

Simulation
Simulation

Specialized equipment control
Specialized equipment control

String processing
String processing

Visual languages
Visual languages

Programming paradigms
Programming paradigms
You have already seen assembly language
You have already seen assembly language
We will study five language paradigms:
We will study five language paradigms:

Top-down (Algol 60 and Fortran)
Top-down (Algol 60 and Fortran)

Functional (Scheme and/or OCaml)
Functional (Scheme and/or OCaml)

Logic (Prolog)
Logic (Prolog)

Object oriented (Smalltalk)
Object oriented (Smalltalk)

Aspect oriented (AspectJ)
Aspect oriented (AspectJ)

Programming language history
Programming language history
Pseudocodes (195X) – Many
Pseudocodes (195X) – Many
Fortran (195X) – IBM, Backus
Fortran (195X) – IBM, Backus
Lisp (196x) – McCarthy
Lisp (196x) – McCarthy
Algol (1958) – Committee (led to Pascal, Ada)
Algol (1958) – Committee (led to Pascal, Ada)
Cobol (196X) – Hopper
Cobol (196X) – Hopper
Functional programming – FP, Scheme, Haskell, ML
Functional programming – FP, Scheme, Haskell, ML
Logic programming – Prolog
Logic programming – Prolog
Object oriented programming – Smalltalk, C++, Python,
Object oriented programming – Smalltalk, C++, Python,
Java
Java
Aspect oriented programming – AspectJ, AspectC++
Aspect oriented programming – AspectJ, AspectC++
Parallel / non-deterministic programming
Parallel / non-deterministic programming

Compilation vs. Translation
Compilation vs. Translation
Translation: does a ‘mechanical’ translation of the source
Translation: does a ‘mechanical’ translation of the source
code
code

No deep analysis of the syntax/semantics of the code
No deep analysis of the syntax/semantics of the code
Compilation: does a thorough understanding and
Compilation: does a thorough understanding and
translation of the code
translation of the code
A compiler/translator changes a program from one
A compiler/translator changes a program from one
language into another
language into another

C compiler: from C into assembly
C compiler: from C into assembly
An assembler then translates it into machine language
An assembler then translates it into machine language

Java compiler: from Java code to Java bytecode
Java compiler: from Java code to Java bytecode
The Java interpreter then runs the bytecode
The Java interpreter then runs the bytecode

Compilation stages
Compilation stages
Scanner
Scanner
Parser
Parser
Semantic analysis
Semantic analysis
Intermediate code generation
Intermediate code generation
Machine-independent code improvement (optional)
Machine-independent code improvement (optional)
Target code generation
Target code generation
Machine-specific code improvement (optional)
Machine-specific code improvement (optional)
For many compilers, the result is assembly
For many compilers, the result is assembly

Which then has to be run through an assembler
Which then has to be run through an assembler
These stages are machine-independent!
These stages are machine-independent!

The generate “intermediate code”
The generate “intermediate code”

Compilation: Scanner
Compilation: Scanner
Recognizes the ‘tokens’ of a program
Recognizes the ‘tokens’ of a program

Example tokens: ( 75 main int { return ; foo
Example tokens: ( 75 main int { return ; foo
Lexical errors are detected here
Lexical errors are detected here

More on this in a future lecture
More on this in a future lecture

Compilation: Parser
Compilation: Parser
Puts the tokens together into a pattern
Puts the tokens together into a pattern

void main ( int argc , char ** argv ) {
void main ( int argc , char ** argv ) {

This line has 11 tokens

It is the beginning of a method
It is the beginning of a method
Syntatic errors are detected here
Syntatic errors are detected here

When the tokens are not in the correct order:
When the tokens are not in the correct order:

int int foo ;
int int foo ;


After the type (int), the parser expects a variable
After the type (int), the parser expects a variable
name
name
Not another type
Not another type

Compilation: Semantic analysis
Compilation: Semantic analysis
Checks for semantic correctness
Checks for semantic correctness
A semantic error:
A semantic error:
foo = 5;
foo = 5;
int foo;
int foo;
In C (and most languages), a variable has to be
In C (and most languages), a variable has to be
declared before it is used
declared before it is used

Note that this is syntactically correct
Note that this is syntactically correct
As both lines are valid lines as far as the parser is concerned
As both lines are valid lines as far as the parser is concerned

Compilation: Intermediate code
Compilation: Intermediate code
generation (and improvement)
Almost all compilers generate intermediate code
Almost all compilers generate intermediate code

This allows part of the compiler to be machine-
This allows part of the compiler to be machine-
independent
independent
That code can then be optimized
That code can then be optimized

Optimize for speed, memory usage, or program
footprint
footprint

Compilation: Target code
Compilation: Target code
The intermediate code is then translated into the
The intermediate code is then translated into the
target code
target code

For most compilers, the target code is assembly
For most compilers, the target code is assembly

For Java, the target code is Java bytecode
For Java, the target code is Java bytecode
That code can then be further optimized
That code can then be further optimized

footprint
footprint

brief intro for Programming Languages 02-chapter-1

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