Overview

Programming Languages

• The computer is unique among “machines” (e.g. lever, pulley, etc.) in that it magnifies our mental force rather than our physical force.
  • Computers can assist with decision making, model and predict outcomes, etc.
• Programming languages are the mechanism for communicating with and commanding the only tool available that magnifies your mind.

Course Structure

• Course has theoretical and practical aspects
• Need both in programming languages
• Reading: both theoretical and practical
• Review sets and Exams: theory
• Programming assignments: practice

How are Languages Implemented?

• Two major strategies:
  • Interpreters - take source code and run it
  • Compilers - translate source code, run result
• Interpreters run programs “as is” with little or no preprocessing
• Compilers to extensive preprocessing

(Short) History of High-Level Languages

• 1953 IBM develops the 701 “Defense Calculator”
• All programming done in assembly
• Problem: software costs exceeded hardware costs

FORTRAN I

• 1954 IBM develops the 704
• John Backus
  • Idea: translate high-level code to assembly
  • Many thought this was impossible
• 1954 - 1957 Fortran I project
• By 1958, greater than 50% of all software is in FORTRAN
• Cut development time dramatically (2 weeks \(\rightarrow\) 2 hours)

FORTRAN I

• The first compiler
  • Produced code almost as good as hand-written assembly
  • Huge impact on computer science
• Led to an enormous body of theoretical work
• Modern compilers keep the outlines of FORTRAN I

Interpreter Phases

1. Lexical Analysis
2. Parsing
3. Semantic Analysis
4. Optimization (optional)
5. Interpret the program

Compiler Phases

1. Lexical Analysis
2. Parsing
3. Semantic Analysis
4. Optimization (optional)
5. Generate machine code
6. Run the machine code

Lexical Analysis (English)

• First step: recognize words
  • smallest unit above letters
• Example: “This is a sentence.”
  • Captial “T” (start of sentence symbol)
  • Space (word separator)
  • Period (end of sentence symbol)

Lexical Analysis

• Lexical Analysis is not trivial.
  • Consider: “How d’you break”this" up?"
• Programming languages are typically more cryptic than English
  • *p->f ++ -.12345e-6
• Lexical analyzer divides program text into “words” or tokens

Parsing (English)

• Once words are understood, the next step is to understand sentence structure
• Parsing = diagramming sentences
  • The diagram is a tree
  • Often annotated with additional information

Diagramming a Sentence

Parsing Programs

• Parsing program expressions is the same
• Consider: if x == y then z = 1; else z = 2;

• Diagrammed:

Semantic Analysis

• Once sentence structure is understood, we can try to understand “meaning”
  • But, meaning is too hard for compilers
• Compilers perform limited analysis to catch inconsistencies: reject bad programs early
• Some do more analysis to improve the performance of the program

Semantic Analysis in English

• Example: “Alice said Eve left her book at home.”
  • What does “her” refer to?
• Example: “Alice said Alice left her book at home.”
  • How many Alices are there?
  • Which one left the book?

Semantic Analysis in Programming

• Programming languages define strict rules to avoid such ambiguities

• Example C++ code:

  {
      int x = 3;
      {
          int x = 4;
          cout << x; // which x gets printed?
      }
  }

Optimization

• No strong counterpart in English, but akin to editing (cf. poems, short stories)

• Automatically modify programs so that they
  • Run faster
  • Use less memory
  • In general, conserve some resource

• “Compilers” studies optimizations in depth

Code Generation

• Produces assembly code (usually)
  • which is then assembled into executables by an assembler
• A translation into another language
  • Analogous to human translation
• “Compilers”: produce machine code

Issues

• Compiling and interpreting are almost this simple, but there are many pitfalls
• Example: How are bad programs handled?
• Language design has a big impact on the compiler
  • Determines what is easy and hard to compile
  • Course theme: trade-offs in language design

Languages Today

• The overall structure of almost every compiler and interpreter follows the above outline
• The proportions have changed since FORTRAN
  • Early: lexing, parsing most complex, expensive
  • Today: optimization dominates all other phases, lexing and parsing are cheap

Trends in Languages

• Optimization for speed is less interesting. But:
  • scientific programs
  • advanced processors
  • small devices where speed equals longer battery life
• Ideas we will discuss are used for improving code reliability:
  • memory safety
  • type safety
  • automatic memory management
  • …

Why Study Programming Languages?

• Prepare for many good jobs
• Increase capacity of expression
• Improve understanding of program behavior
• Increase ability to learn new languages
• Learn to build a large and reliable system
• Computers are the only tools that increase cognitive power, so learn to

What Will You Do In This Class?

• Reading
• Learn about different kinds of languages
  • Imperative vs. Functional vs. Object-Oriented
  • Static typing vs. Dynamic typing
  • etc.
• Gain exposure to new languages
• Write an interpreter