CS334 Lecture 5 Read carefully Chapter 4 in text as we will not cover all aspects in class. Just skim lightly over sections 4.6 & 4.7.

Major elements of programming languages:
Syntax, Semantics, Pragmatics

Syntax: Readable, writable, easy to translate, unambiguous, ...

Formal Grammars: Backus & Naur, Chomsky

<expression>  ::= <term> | <expression> <addop> <term>
<term>        ::= <factor> | <term> <multop><factor>
<factor>      ::= <identifier> | <literal> | (<expression>)
<identifier>  ::= a | b | c | d
<literal>     ::= <digit> | <digit> <literal>
<digit>       ::= 0 | 1 | 2 | ... | 9
<addop>       ::= + | - | or
<multop>      ::= * | / | div | mod | and
Generates: a + b * c + b - parse tree

Grammar gives precedence and which direction op's associate

Extended BNF handy:

Syntax diagrams - alternative to BNF,
Syntax diagrams are never recursive, use "loops" instead.

Problems with Ambiguity

Suppose given grammar:
	<statement> ::= <unconditional> | <conditional>

<unconditional> ::= <assignment> | <for loop> |

begin {<statement>} end

<conditional> ::= if <expression> then <statement> |

if <expression> then <statement> else <statement>

How do you parse: if exp1 then if exp2 then stat1 else stat2 ?

Could be

  1. if exp1 then (if exp2 then stat1 else stat2) or

  2. if exp1 then (if exp2 then stat1) else stat2

I.e. What happens if exp1 is true and exp2 is false?


Pascal rule: else attached to nearest then

To get second form, write:

	if exp1 then 
			if exp2 then stat1 
C has similar ambiguity.

MODULA-2 and ALGOL 68 require "end" to terminate conditional:

  1. if exp1 then if exp2 then stat1 else stat2 end end

  2. if exp1 then if exp2 then stat1 end else stat2 end

(Algol 68 actually uses fi instead of end)

Why isn't it a problem in ML?

Ambiguity in general is undecidable

Chomsky developed mathematical theory of programming languages:

BNF (or syntax diagrams) = context-free can be recognized by push-down automata

Not all aspects of programming language syntax are context-free.

Formal description of syntax allows:
  1. programmer to generate syntactically correct programs

  2. parser to recognize syntactically correct programs

Parser-generators (also lexical analysis)- LEX, YACC (available in C and many other languages, e.g., ML), Cornell Program Synthesizer Generator


Programming language creates a virtual machine for programmer

Dijkstra: Originally we were obligated to write programs so that a computer could execute them. Now we write the programs and the computer has the obligation to understand and execute them.

Progress in programming language design marked by increasing support for abstraction.

Computer at lowest level is set of charged particles racing through wires w/ memory locations set to one and off - very hard to deal with.

In computer organization look at higher level of abstraction: interpret sequences of on/off as data (reals, integers, char's, etc) and as instructions.

Computer looks at current instruction and contents of memory, then does something to another chunk of memory (incl. registers, accumulators, program counter, etc.)

When write Pascal (or other language) program - work with different virtual machine.

Language creates the illusion of more sophisticated virtual machine.

Pure translators




Execution of program w/ compiler:


We will speak of virtual machine defined by a language implementation.

Machine language of virtual machine is set of instructions supported by translator for language.

Layers of virtual machines on Mac: Bare 680x0 chip, OpSys virtual machine, MacPascal (or Lightspeed Pascal) machine, application program's virtual machine.

We will describe language in terms of virtual machine

Slight problem:

May lead to different implementations of same language - even on same machine.

Problem : How can you ensure different implementations result in same semantics?

Sometimes virtual machines made explicit:

Compilers and Interpreters

While exist few special purpose chips which execute high-level languages (LISP machine) most have to be translated into machine language.

Two extreme solutions:

Pure interpreter: Simulate virtual machine (our approach to run-time semantics)

		Get next statement
		Determine action(s) to be executed
		Call routine to perform action
	UNTIL done

Pure Compiler:

  1. Translate all units of program into object code (say, in machine language)

  2. Link into single relocatable machine code

  3. Load into memory

Comparison of Compilation vs Interpretation

compiler interpreter
Only translate each statement once. Translate only if executed.
Speed of execution. Error messages tied to source.
More supportive environment.
Only object code in memory when executing.
May take more space because of expansion.
Must have interp. in memory when executing (but source may be more compact)

Rarely have pure compiler or interpreter.

Can go farther and compile into intermediate code (e.g., P-code) and then interpret.

In FORTRAN, Format statements (I/O) are always interpreted.

Overview of structure of a compiler

Two primary phases:
Break into lexical items, build parse tree, generate simple intermediate code (type checking)

Optimization (look at instructions in context), code generation, linking and loading.

Lexical analysis:
Break source program into lexical items, e.g. identifiers, operation symbols, key words, punctuation, comments, etc. Enter id's into symbol table. Convert lexical items into internal form - often pair of kind of item and actual item (for id, symbol table reference)

Syntactical analysis:
Use formal grammar to parse program and build tree (either explicitly or implicitly through stack)

Semantic analysis:
Update symbol table (e.g., by adding type info). Insert implicit info (e.g., resolve overloaded ops's - like "+"), error detection - type-checking, jumps into loops, etc. Traverse tree generating intermediate code

Catch adjacent store-reload pairs, eval common sub-expressions, move static code out of loops, allocate registers, optimize array accesses, etc.
		for i := .. do ...
			for j:= 1 to n do
				A[i,j] := ....
Code generation:
Generate real assembly or machine code (now sometimes generate C code)

Linking & loading:
Get object code for all pieces of program (incl separately compiled modules, libraries, etc.). Resolve all external references - get locations relative to beginning location of program. Load program into memory at some start location - do all addressing relative to base address.

Symbol table: Contains all identifier names, kind of id (vble, array name, proc name, formal parameter), type of value, where visible, etc. Used to check for errors and generate code. Often thrown away at end of compilation, but may be held for error reporting or if names generated dynamically.

Like to have easily portable compilers front-end vs back-end

Front-end generate intermediate code and do some peep-hole optimization

Back-end generate real code and do more optimization.


Meaning of a program (once know it is syntactically correct). Work with virtual (or abstract) machine when discuss semantics of programming language constructs. Run program by loading it into memory and initializing ip to beginning of program

Official language definitions: Standardize syntax and semantics - promote portability.

Often better to standardize after experience. -- Ada standardized before a real implementation.

Common Lisp, Scheme, ML now standardized, Fortran '9x.

Good formal description of syntax, semantics still hard.

Backus, in Algol 60 Report promised formal semantics.