This is my study notes of CS143 Compilers offered by Stanford Online and the resources can also be found on the web homepage. The notes below contain my summaries, lecture materials, and some assessments answers.

## Intro

### The Structure of a Compiler

1. Lexical Analysis - Syntactic
2. Parsing - Syntactic
3. Semantic Analysis - Types scope
4. Optimization
5. Code Generation - Translation

## Lexical Analysis

Tokens correspond to sets of strings.

• Identifier: strings of letters or digits, starting with a letter
• Integer: a non-empty string of digits
• Keyword: “else” or”if” or “begin” or …
• Whitespace: a non-empty sequence of blanks, newlines, and tabs

An implementation must do two things:

1. Recognize substrings corresponding to tokens
2. Return the value or lexeme of the token – The lexeme is the substring

The goal of lexical analysis is to:

• Partition the input string into lexemes
• Identify the token of each lexeme

Left-to-right scan => lookahead sometimes required

### Regular Languages

Def. The regular expressions over $\Sigma$ are the smallest set of expressions including:

• ε = {“”}
• ‘c’ = {“c”} where c $\in$ $\Sigma$
• A + B = A U B where A, B are rexp over $\Sigma$
• A* = A is a rexp over $\Sigma$

L:Exp -> Sets of Strings

Meaning is many (syntax) to one (semantic).

### Lexical Specification

Regular expressions to Lexial specification - Ambiguities:

Rule: Pick longest possible string in L(R) – The “maximal munch”

### Finite Automata

Regular expressions = specification

Finite automata = implementation

#### Deterministic Finite Automata (DFA)

– One transition per input per state – No ε-moves

#### Nondeterministic Finite Automata (NFA)

– Can have multiple transitions for one input in a given state – Can have ε-moves

Execution of Finite Automata:

• A DFA can take only one path through the state graph – Completely determined by input
• NFAs can choose

• Whether to make ε-moves
• Which of multiple transitions for a single input to take

#### NFA vs. DFA

• NFAs and DFAs recognize the same set of languages (regular languages)
• DFAs are faster to execute – There are no choices to consider (but less compact)
• For a given language NFA can be simpler than DFA (slower but more concise)
• DFA can be exponentially larger than NFA

High-level sketch: Lexical Specification => RegExp => NFA => DFA => Table-driven Implementation of DFA

#### NFA to DFA

##### The Trick
• Simulate the NFA
• Each state of DFA = a non-empty subset of states of the NFA
• Start state = the set of NFA states reachable through ε-moves from NFA start state
• Add a transition S →a S’ to DFA iff S’ is the set of NFA states reachable from any state in S after seeing the input a, considering ε-moves as well
##### Remark
• An NFA may be in many states at any time
• How many different states? - N states
• If there are N states, the NFA must be in some subset of those N states
• How many subsets are there? – ${2}^N$ - 1 (finite set of possible configurations) = finitely many

## Parsing

Phase Input Output
Lexer String of characters String of tokens
Parser String of tokens Parse tree

### Context-free grammars (CFG’s)

A CFG consists of:

• A set of terminals T
• A set of non-terminals N
• A start symbol S (a non-terminal)
• A set of productions: $X \to dY_1Y_2 … Y_n$ where X $\in$ N and $Y_i$ $\in$ T $\cup$ N $\cup$ {$\epsilon$}

The language of a CFG:

 Let G be a context-free grammar with start symbol S. Then the language of G is: {$a_1$ … $a_n$ S ${\to}^*$ $a_1$ … $a_n$ and every $a_i$ is a terminal }

Terminals are so-called because there are no rules for replacing them;

• Once generated, terminals are permanent;
• Terminals ought to be tokens of the language.

### Derivations

Notes on derivations:

• A parse tree has

• Terminals at the leaves
• Non-terminals at the interior nodes
• An in-order traversal of the leaves is the original input
• The parse tree shows the association of operations, the input string does not

### Ambiguity

A grammar is ambiguous if it has more than one parse tree for some string; Equivalently, there is more than one right-most or left-most derivation for some string.

### Error Handling

Purpose of the compiler is

• To detect non-valid programs
• To translate the valid ones

Many kinds of possible errors (e.g. in C)

Error kind Example Detected by
Lexical … \$ … Lexer
Syntax … x *% … Parser
Semantic … int x; y = x(3); … Type checker

#### Syntax Error Handling

Error handler should:

• Recover from an error quickly
• Not slow down compilation of valid code

Approaches - From simple to complex

• Panic mode
• Error productions
• Automatic local or global correction

### Top-Down Parsing & Recursive Decent Parsing

Start with top-level non-terminal E – Try the rules for E in order

#### Left Recursion

In general S → S α1 $\|$$\|$ S αn $\|$ β1 $\|$$\|$ βm ; All strings derived from S start with one of β1,…,βm and continue with several instances of α1,…,αn; Rewrite as:

S → β1 S’ | … | βm S’
S’ → α1 S’ | … | αn S’ | ε


### Bottom-Up Parsing

Like recursive-descent but parser can “predict” which production to use

• By looking at the next few tokens
• No backtracking

Predictive parsers accept LL(k) grammars

• L means “left-to-right” scan of input
• L means “leftmost derivation”
• k means “predict based on k tokens of lookahead”
• In practice, LL(1) is used

### LL(1) vs. Recursive Descent

• In recursive-descent,

• At each step, many choices of production to use
• Backtracking used to undo bad choices
• In LL(1),

• At each step, only one choice of production
• That is

• When a non-terminal A is leftmost in a derivation
• The next input symbol is t
• There is a unique production A → α to use – Or no production to use (an error state)
• LL(1) is a recursive descent variant without backtracking

Left-Factoring: Factor out common prefixes of productions

#### Notes on LL(1) Parsing Tables

If any entry is multiply defined then G is not LL(1)

• If G is ambiguous
• If G is left recursive
• If G is not left-factored
• And in other cases as well

Most programming language CFGs are not LL(1)

Bottom-up parsing is more general than top-down parsing

• And just as efficient
• Builds on ideas in top-down parsing

Bottom-up is the preferred method.

### Shift-Reducing Parsing

Bottom Up Parsers construct rightmost derivations.

SLR: Simple LR parser -> LR(0). Input from Left; derivation from Rightmost

Handles formalize the intuition:

• A handle is a reduction that allows further reductions back to the start symbol.
• We only want to reduce at handles.

To recognise a viable prefixes, we must:

• recognize a sequence of partial rhs’s of productions, where
• each partial rhs can eventually reduce to part of the missing suffix of its predecessor

(1). A bottom-up parser traces a rightmost derivation in reverse

(2). In shift-reduce parsing, handles appear only at the top of the stack, never inside

(3). For any grammar, the set of viable prefixes is a regular language.

An item is a production with a "." somewhere on the rhs.

The states of the DFA are “canonical collections of items” or “canonical collections of LR(0) items”.

X -> b.r is valid for a viable prefix ab if S’ -> aXw -> abrw by a right-most derivation; after parsing ab, the valid items are the possible tops of the stack of items.