Why study programming
languages?
• Increased capacity to express ideas
– Constructs we can express can be constrained by the language we know
• Improved background for choosing appropriate language
– Should choose language appropriate for task not just the one that we know
• Increased ability to learn new languages
– Learning new languages and learning language fundamentals enables us to
learn other languages more easily
• Better understanding of the significance of implementation
– Aids in finding bugs
– Enables us to write more efficient code
•
Better use of languages that are already known
– looking at the concept of languages (threads, exceptions, polymorphism)
encourages uses of those things in languages already known (Java)
• Overall advancement of computing
– Educated people will encourage use of good languages
Section 1.1
1
Computer Applications & Languages
• Scientific
– Number crunching, weather forecasting, codebreaking,
plane/missile trajectories
– Language requirements:
•
•
•
•
Real numbers
Large amount of calculations (hence efficient)
Arrays, matrices
Selection, Counting loops
– Languages:
• Assembly, Fortran, Algol and descendants of Algol (Pascal,
Modula, Ada, PL/I)
Section 1.2
2
Computer Applications & Languages
• Scientific Applications
– Language requirements: reals, efficiency, arrays, loops
– Languages: Fortran, assembly, Algol
• Business
– Financial transactions
– Language requirements:
• Report production
• Exact (but low) precision (e.g., money)
• Character data
– Languages: COBOL, RPG
Section 1.2
3
Computer Applications & Languages
•
•
Scientific Applications: reals, efficiency, arrays, loops
Fortran, assembly, Algol
Business Applications: reports, decimal precision, characters
COBOL, RPG
• Artificial Intelligence
– Deductive systems, reasoning, machine learning
– Language requirements:
• Symbolic computation (“names” not numbers)
• Lists of data (not arrays)
• Recursion
– Languages: LISP, Scheme, Prolog
Section 1.2
4
Computer Applications & Languages
•
•
•
Scientific Applications: reals, efficiency, arrays, loops
Fortran, assembly, Algol
Business Applications: reports, decimal precision, characters
COBOL, RPG
AI Applications: Symbolic computation, Lists of data, Recursion
LISP, Scheme, Prolog
• Systems Programming
– Operating systems, compilers, debuggers, etc.
– Language requirements:
• FAST execution
• External device interfaces
– Languages: C, Assembly
Section 1.2
5
Computer Applications & Languages
•
•
•
•
Scientific Applications: reals, efficiency, arrays, loops
Fortran, assembly, Algol
Business Applications: reports, decimal precision, characters
COBOL, RPG
AI Applications: Symbolic computation, Lists of data, Recursion
LISP, Scheme, Prolog
Systems Programming: fast, external devices
C, Assembly
• Web Software
– Language requirements: dynamic web content, database
access
– Languages: XHTML, JavaScript, PHP, Ruby, Java
Section 1.2
6
Language Influences
• Architecture
– von Neumann
• Memory
• CPU
• ALU
• Programming Methodologies
– Unstructured
– Procedure-oriented (top-down, functions, …)
– Data-oriented (ADT, OO)
Section 1.4
7
Language Categories (most
important ones)
•
•
•
•
Imperative
Functional
Logic
Object oriented (extension of imperative)
Section 1.5
8
Imperative Programming
Languages
• Central features are variables and
assignment statements
• Design based on the von Neumann
architecture
– Variables model memory cells
– Assignment statement models the “piping
operation” – operands piped from memory to
CPU; result piped from CPU to memory
Section 1.5
9
Functional Programming
Languages
• Main features are functions and function
calls (recursion instead of iteration)
• Design is based upon mathematical
functions
Section 1.5
10
Logic programming languages
• Also called rule-based languages and
declarative languages
• Programmer gives rules that specify what a
solution looks like and not how desired
solution is achieved
Section 1.5
11
Object Oriented Programming
Languages
• Provides a feature for building objects
• Provides a method for specifying
inheritance
• Provides polymorphism
– Same name for different functions (method
overriding)
– Dynamic binding of function call to function
body
Section 1.5
12
Implementation methods
• Compilation
• Interpretation
• Hybrid
Section 1.7
13
Compilation
• Compiler translates program into machine
language that is executed on the computer
• Execution is fast
Section 1.7
14
source
Compilation
machine code
Lexical
analyzer
Syntax
analyzer
Optimizer
Linker
executable
Semantic analyzer/
Intermediate code
generation
Code
generator
machine code
Section 1.7
15
Interpreter
• Pure interpretation means there is no
translation of the source program
• Program called an interpreter is executed by
the computer
• Interpreter fetches/decodes/executes source
program
• Slower and requires more space than the
compilation process
Section 1.7
16
Hybrid
• Source program is translated into an
intermediate code that is then interpreted
• Examples
– Java is translated into byte code that can be
interpreted
– Perl is compiled before execution in order to
detect compilation errors (unlike shell
languages)
Section 1.7
17