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Why Study Programming Languages? Programming Domains Language Evaluation Influences on Language Design Language Categories Design Trade-Offs Implementation Methods Summary

Preliminaries

Why Study Programming Languages?

• Increased ability to express ideas.
• Improved background for choosing appropriate languages.
• Increased ability to learn new languages.
• Better understanding of significance of implementation.
• Better use of languages that are already known.

Programming Domains

• Scientific
  • Floating Point
  • Arrays
  • Fortran
• Business
  • Fixed Point
  • Characters
  • Reports
  • COBOL
• AI
  • Symbol Manipulation
  • Linked Lists
  • LISP
• Systems
  • Efficiency
  • Direct Memory Access
  • C
• Web
  • Markup (HTML, CSS)
  • Client Side (Javascript)
  • Server Side (PHP)
  • Java

Language Evaluation

• Readability
  • Simplicity
  • Orthogonality
  • Data Types
  • Syntax
  • Meaningful keywords
• Writeability
  • Simplicity & Orthogonality
  • Support for Abstraction
  • Expressivity
• Reliability
  • Type Checking
  • Exception Handling
  • Aliasing
• Cost
  • Training
  • Reliability
  • Maintenance
• Others
  • Portability
  • Generality
  • Well-Definedness

Influences on Language Design

Computer Architecture

• Data and programs stored.
• Basis for imperative languages.
• Variables are memory cells.
• Code is instructions.
• Fetch-execute Cycle:
  1. Initialize program counter.
  2. Repeat Forever:
     1. Fetch instruction at counter.
     2. Increment counter.
     3. Decode the instruction.
     4. Execute the instruction.

Programming Methodologies

• Earliest languages were for simple programs that had to be efficient.
• In 1960s readability was stressed, structured programs.
• In 1970s data-oriented programming and abstraction.
• In 1980s object-oriented programming: inheritance, polymorphism.
• In 2000s, dynamic programming and rapid development.

Language Categories

• Imperative
• Variables, assignment, iteration are key.
• Computation done by updating variables.
• Examples: C, C++, Java, Python, PHP, Basic
• Most common type.
• Functional
• Computation done by applying functions to parameters.
• Based on Lambda Calculus.
• Does not rely on modifying variables.
• Recursion more common than iteration.
• Examples: LISP, Haskell, ML
• Logic
• Rule-based.
• Specifies rules in any order, and computation follows the rules.
• Prolog.
• Markup Hybrids.
• Languages for encoding data with programming features.
• HTML not a programming language.
• Examples: JSTL, XSLT, Postscript, TeX
• Domain-Specific
• Languages developed for a particular use.
• Examples: SQL, Lex & Yacc, Awk, Makefile

Design Trade-Offs

• Reliability vs. Efficiency
• Checking array bounds.
• Checking for exceptions.
• Readability vs. Writability
• Compactness.
• Perl:

  ''=~('(?{'.(']/)@_]'^'-]@.+}').'"'.(')_@}'^'`>._').',$/})')
  

  (This program prints "Ian").
• APL:

  (~R∊R∘.×R)/R←1↓⍳R
  

  (This program finds all prime numbers from 1 to R).
• Flexibility vs. Reliability
• Pointers.

Implementation Methods

Different programming languages expose the underlying machine in different ways.

Compilation

• Programs are translated into machine language.
• Faster execution.
• Takes time to compile.
• Can be less flexible.

Phases:

1. Lexical Analysis
2. Syntax Analysis
3. Intermediate Code Generation
4. General Optimizations
5. Machine Code
6. Specific Optimizations

Interpretation

• An interpreter program runs the code.
• Less efficient.
• Very flexible.
• Easier to implement.

Hybrid Systems

• Compromise between compilation and interpretation.
• Compiles down to "bytecode" which is interpreted.
• Just in Time Compilation.
• Faster than pure interpretation.

Summary

• There are different types of programming languages.
• Knowing more languages makes you a better programmer.
• Languages have different goals and uses.
• Programming languages are also determined by their tools and environments.

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