# Compilers

Start Lecture #4

Example: Let both the alphabet and the language L be {0,1,2,3,4,5,6,7,8,9}. More formally, I sould say the alphabet A is {0,1,2,3,4,5,6,7,8,9} and the language L is the set of all strings of length one over A. L+ gives all unsigned integers, but with some ugly versions. It has 3, 03, 000003, etc.
{0} ∪ ( {1,2,3,4,5,6,7,8,9} ({0,1,2,3,4,5,6,7,8,9}* ) ) seems better.

In these notes I may write * for * and + for +, but that is strictly speaking wrong and I will not do it on the board or on exams or on lab assignments.

Example: Let the alphabet be {a,b,c} and let the language L be {a,b} (really {"a","b"}). What is L*={a,b}*?
Furthermore, {a,b}+ is {a,b,aa,ab,ba,bb,aaa,aab,aba,abb,baa,...}, and
{ε,a,b}* is {ε,a,b,aa,ab,ba,bb,...}={a,b}*, and
{ε,a,b}+ = {ε,a,b}* = {a,b}*.

The book gives other examples based on L={letters} and D={digits}, which you should read.

### 3.3.3: Regular Expressions

The idea is that the regular expressions over an alphabet consist of
the alphabet, and expressions using union, concatenation, and *,
but it takes more words to say it right. For example, I didn't include (). Note that (A ∪ B)* is definitely not A* ∪ B* (* does not distribute over ∪) so we need the parentheses.

The book's definition includes many () and is more complicated than I think is necessary. However, it has the crucial advantages of being correct and precise.

The wikipedia entry doesn't seem to be as precise.

I will try a slightly different approach, but note again that there is nothing wrong with the book's approach (which appears in both first and second editions, essentially unchanged).

Definition: The regular expressions and associated languages over an alphabet consist of
1. ε, the empty string; the associated language L(ε) is {ε}, which is not φ.
2. Each symbol x in the alphabet; L(x) is {x}.
3. rs for all regular expressions (REs) r and s; L(rs) is L(r)L(s).
4. r|s for all REs r and s; L(r|s) is L(r) ∪ L(s).
5. r* for all REs r; L(r*) is (L(r))*.
6. (r) for all REs r; L((r)) is L(r).

Parentheses, if present, control the order of operations. Without parentheses the following precedence rules apply.

The postfix unary operator * has the highest precedence. The book mentions that it is left associative. (I don't see how a postfix unary operator can be right associative or how a prefix unary operator such as unary minus could be left associative.)

Concatenation has the second highest precedence and is left associative.

| has the lowest precedence and is left associative.

The book gives various algebraic laws (e.g., associativity) concerning these operators.

As mentioned above, we don't need the positive closure since, for any RE,

```    r+ = rr*.
```

Homework: 2(a-d), 4.

Examples
Let the alphabet A={a,b,c}. Write a regular expression representing the language consisting of all words with

1. at least one c.
2. every b followed by a c.
3. the same number of b's as c's

1. (a|b|c)*c(a|b|c)*
2. (a|bc|c)*
3. IMPOSSIBLE! Regular expressions can't count (discussed later in the course).

### 3.3.4: Regular Definitions

These look like the productions of a context free grammar we saw previously, but there are differences. Let Σ be an alphabet, then a regular definition is a sequence of definitions

```      d1 → r1
d2 → r2
...
dn → rn
```
where the d's are unique and not in Σ and
ri is a regular expressions over Σ ∪ {d1,...,di-1}.

Note that each di can depend on all the previous d's.

Note also that each di can not depend on following d's. This is an important difference between regular definitions and productions (the latter are more powerful).

Example: C identifiers can be described by the following regular definition

```    letter_ → A | B | ... | Z | a | b | ... | z | _
digit → 0 | 1 | ... | 9
CId → letter_ ( letter_ | digit)*
```

Regular definitions are just a convenience; they add no power to regular expressions. The C identifier example can be done simply as a regular expression by simply plugging in the earlier definitions to the later ones.

### 3.3.5: Extensions of Regular Expressions

There are many extensions of the basic regular expressions given above. The following three, especially the third, will be occasionally used in this course as they are useful for lexical analyzers.

All three are simply shorthand. That is, the set of possible languages generated using the extensions is the same as the set of possible languages generated without using the extensions.

1. One or more instances. This is the positive closure operator + mentioned above.
2. Zero or one instance. The unary postfix operator ? defined by
r? = r | ε for any RE r.
3. Character classes. If a1, a2, ..., an are symbols in the alphabet, then
[a1a2...an] = a1 | a2 | ... | an. In the special case where all the a's are consecutive, we can simplify the notation further to just [a1-an].

Examples:

1. C-language identifiers
```	letter_ → [A-Za-z_]
digit → [0-9]
CId → letter_ ( letter_ | digit )*
```
2. Unsigned integer or floating point numbers
```	digit → [0-9]
digits → digit+
number → digits (. digits)?(E[+-]? digits)?
```
This is actually fairly restrictive. For example, it doesn't permit 3. as a number.

Homework: 1(a). You might need a reminder on the various floating point numerical constants in C. The following is section A2.5.3 from Kernighan and Richie, The C Programming Language, 2ed. Appendix A is entitled Reference Manual.

A2.5.3 Floating Constants

A floating constant consists of an integer part, a decimal point, a fraction part, an e or E, an optionally signed integer exponent and an optional type suffix, one of f, F, l, or L. The integer and fraction parts both consist of a sequence of digits. Either the integer or the the fraction part (not both) may be missing; either the decimal point or the e [or E—ajg] and the exponent (not both) may be missing. The type is determined by the suffix; F or f makes it float, L or l makes it long double; otherwise it is double.

## 3.4: Recognition of Tokens

Our current goal is to perform the lexical analysis needed for the following grammar.

```    stmt → if expr then stmt
| if expr then stmt else stmt
| ε
expr → term relop term   // relop is relational operator =, >, etc
| term
term →  id
| number
```

Recall that the terminals are the tokens, the nonterminals produce terminals.

A regular definition for the terminals is

```    digit → [0-9]
digits → digits+
number → digits (. digits)? (E[+-]? digits)?
letter → [A-Za-z]
id → letter ( letter | digit )*
if → if
then → then
else → else
relop → < | > | <= | >= | = | <>
```
LexemeTokenAttribute
Whitespacews
ifif
thenthen
elseelse
An identifieridPointer to table entry
A numbernumberPointer to table entry
<relopLT
<=relopLE
=relopEQ
<>relopNE
>relopGT
>=relopGE

On the board show how this can be done with just REs.

We also want the lexer to remove whitespace so we define a new token

```    ws → ( blank | tab | newline ) +
```
where blank, tab, and newline are symbols used to represent the corresponding ascii characters.

Recall that the lexer will be called by the parser when the latter needs a new token. If the lexer then recognizes the token ws, it does not return it to the parser but instead goes on to recognize the next token, which is then returned. Note that you can't have two consecutive ws tokens in the input because, for a given token, the lexer will match the longest lexeme starting at the current position that yields this token. The table on the right summarizes the situation.

For the parser, all the relational ops are to be treated the same so they are all the same token, relop. Naturally, other parts of the compiler, for example the code generator, will need to distinguish between the various relational ops so that appropriate code is generated. Hence, they have distinct attribute values.

### 3.4.1: Transition Diagrams

A transition diagram is similar to a flowchart for (a part of) the lexer. We draw one for each possible token. It shows the decisions that must be made based on the input seen. The two main components are circles representing states (think of them as decision points of the lexer) and arrows representing edges (think of them as the decisions made).

The transition diagram (3.13) for relop is shown on the right.

1. The double circles represent accepting or final states at which point a lexeme has been found. There is often an action to be done (e.g., returning the token), which is written to the right of the double circle.
2. If we have moved one (or more) characters too far in finding the token, one (or more) stars are drawn.
3. An imaginary start state exists and has an arrow coming from it to indicate where to begin the process.

It is fairly clear how to write code corresponding to this diagram. You look at the first character, if it is <, you look at the next character. If that character is =, you return (relop,LE) to the parser. If instead that character is >, you return (relop,NE). If it is another character, return (relop,LT) and adjust the input buffer so that you will read this character again since you have not used it for the current lexeme. If the first character was =, you return (relop,EQ).

### 3.4.2: Recognition of Reserved Words and Identifiers

The next transition diagram corresponds to the regular definition given previously.

Note again the star affixed to the final state.

Two questions remain.

1. How do we distinguish between identifiers and keywords such as then, which also match the pattern in the transition diagram?
2. What is (gettoken(), installID())?

We will continue to assume that the keywords are reserved, i.e., may not be used as identifiers. (What if this is not the case—as in Pl/I, which had no reserved words? Then the lexer does not distinguish between keywords and identifiers and the parser must.)

We will use the method mentioned last chapter and have the keywords installed into the identifier table prior to any invocation of the lexer. The table entry will indicate that the entry is a keyword.

installID() checks if the lexeme is already in the table. If it is not present, the lexeme is installed as an id token. In either case a pointer to the entry is returned.

gettoken() examines the lexeme and returns the token name, either id or a name corresponding to a reserved keyword.

The text also gives another method to distinguish between identifiers and keywords.

### 3.4.3: Completion of the Running Example

So far we have transition diagrams for identifiers (this diagram also handles keywords) and the relational operators. What remains are whitespace, and numbers, which are respectively the simplest and most complicated diagrams seen so far.

#### Recognizing Whitespace

The diagram itself is quite simple reflecting the simplicity of the corresponding regular expression.

• The delim in the diagram represents any of the whitespace characters, say space, tab, and newline.
• The final star is there because we needed to find a non-whitespace character in order to know when the whitespace ends and this character begins the next token.
• There is no action performed at the accepting state. Indeed the lexer does not return to the parser, but starts again from its beginning as it still must find the next token.

#### Recognizing Numbers

This certainly looks formidable, but it is not that bad; it follows from the regular expression.

In class go over the regular expression and show the corresponding parts in the diagram.

When an accepting states is reached, action is required but is not shown on the diagram. Just as identifiers are stored in a identifier table and a pointer is returned, there is a corresponding number table in which numbers are stored. These numbers are needed when code is generated. Depending on the source language, we may wish to indicate in the table whether this is a real or integer. A similar, but more complicated, transition diagram could be produced if the language permitted complex numbers as well.

Homework: 1 (only the ones done before).

### 3.4.4: Architecture of a Transition-Diagram-Based Lexical Analyzer

The idea is that we write a piece of code for each decision diagram. I will show the one for relational operations below. This piece of code contains a case for each state, which typically reads a character and then goes to the next case depending on the character read. The numbers in the circles are the names of the cases.

Accepting states often need to take some action and return to the parser. Many of these accepting states (the ones with stars) need to restore one character of input. This is called retract() in the code.

What should the code for a particular diagram do if at one state the character read is not one of those for which a next state has been defined? That is, what if the character read is not the label of any of the outgoing arcs? This means that we have failed to find the token corresponding to this diagram.

The code calls fail(). This is not an error case. It simply means that the current input does not match this particular token. So we need to go to the code section for another diagram after restoring the input pointer so that we start the next diagram at the point where this failing diagram started. If we have tried all the diagram, then we have a real failure and need to print an error message and perhaps try to repair the input.

Note that the order the diagrams are tried is important. If the input matches more than one token, the first one tried will be chosen.

```    TOKEN getRelop()                        // TOKEN has two components
TOKEN retToken = new(RELOP);          // First component set here
while (true)
switch(state)
case 0: c = nextChar();
if (c == '<')      state = 1;
else if (c == '=') state = 5;
else if (c == '>') state = 6;
else fail();
break;
case 1: ...
...
case 8: retract();  // an accepting state with a star
retToken.attribute = GT;  // second component
return(retToken);
```

#### Alternate Methods

The book gives two other methods for combining the multiple transition-diagrams (in addition to the one above).

1. Unlike the method above, which tries the diagrams one at a time, the first new method tries them in parallel. That is, each character read is passed to each diagram (that hasn't already failed). Care is needed when one diagram has accepted the input, but others still haven't failed and may accept a longer prefix of the input.
2. The final possibility discussed, which appears to be promising, is to combine all the diagrams into one. That is easy for the example we have been considering because all the diagrams begin with different characters being matched. Hence we just have one large start with multiple outgoing edges. It is more difficult when there is a character that can begin more than one diagram.

## 3.5: The Lexical Analyzer Generator Lex

We are skipping 3.5 because

1. We will be writing our lexer from scratch.
2. What is here is not enough to learn how to use lex/flex.
3. If you are interested in learning how to use them you need to read (at least) the manual.

The newer version is called flex, the f stands for fast. I checked and both lex and flex are on the cs machines. I will use the name lex for both.

Lex is itself a compiler that is used in the construction of other compilers (its output is the lexer for the other compiler). The lex language, i.e, the input language of the lex compiler, is described in the few sections. The compiler writer uses the lex language to specify the tokens of their language as well as the actions to take at each state.

### 3.5.1: Use of Lex

Let us pretend I am writing a compiler for a language called pink. I produce a file, call it lex.l, that describes pink in a manner shown below. I then run the lex compiler (a normal program), giving it lex.l as input. The lex compiler output is always a file called lex.yy.c, a program written in C.

One of the procedures in lex.yy.c (call it pinkLex()) is the lexer itself, which reads a character input stream and produces a sequence of tokens. pinkLex() also sets a global value yylval that is shared with the parser. I then compile lex.yy.c together with a the parser (typically the output of lex's cousin yacc, a parser generator) to produce say pinkfront, which is an executable program that is the front end for my pink compiler.

### 3.5.2: Structure of Lex Programs

The general form of a lex program like lex.l is

```      declarations
%%
translation rules
%%
auxiliary functions
```

The lex program for the example we have been working with follows (it is typed in straight from the book).

```      %{
/* definitions of manifest constants
LT, LE, EQ, NE, GT, GE,
IF, THEN, ELSE, ID, NUMBER, RELOP */
%}

/* regular definitions */
delim     [ \t\n]
ws        {delim}*
letter    [A-Za-z]
digit     [0-9]
id        {letter}({letter}{digit})*
number    {digit}+(\.{digit}+)?(E[+-]?{digit}+)?

%%

{ws}      {/* no action and no return */}
if        {return(IF);}
then      {return(THEN);}
else      {return(ELSE);}
{id}      {yylval = (int) installID(); return(ID);}
{number}  {yylval = (int) installNum(); return(NUMBER);}
"<"       {yylval = LT; return(RELOP);}
"<="      {yylval = LE; return(RELOP);}
"="       {yylval = EQ; return(RELOP);}
"<>"      {yylval = NE; return(RELOP);}
">"       {yylval = GT; return(RELOP);}
">="      {yylval = GE; return(RELOP);}

%%

int installID() {/* function to install the lexeme, whose first
character is pointed to by yytext, and whose
length is yyleng, into the symbol table and
return a pointer thereto */
}

int installNum() {/* similar to installID, but puts numerical
constants into a separate table */
```

The first, declaration, section includes variables and constants as well as the all-important regular definitions that define the building blocks of the target language, i.e., the language that the generated lexer will analyze.

The next, translation rules, section gives the patterns of the lexemes that the lexer will recognize and the actions to be performed upon recognition. Normally, these actions include returning a token name to the parser and often returning other information about the token via the shared variable yylval.

If a return is not specified the lexer continues executing and finds the next lexeme present.

#### Comments on the Lex Program

Anything between %{ and %} is not processed by lex, but instead is copied directly to lex.yy.c. So we could have had statements like

```      #define LT 12
#define LE 13
```

The regular definitions are mostly self explanatory. When a definition is later used it is surrounded by {}. A backslash \ is used when a special symbol like * or . is to be used to stand for itself, e.g. if we wanted to match a literal star in the input for multiplication.

Each rule is fairly clear: when a lexeme is matched by the left, pattern, part of the rule, the right, action, part is executed. Note that the value returned is the name (an integer) of the corresponding token. For simple tokens like the one named IF, which correspond to only one lexeme, no further data need be sent to the parser. There are several relational operators so a specification of which lexeme matched RELOP is saved in yylval. For id's and numbers's, the lexeme is stored in a table by the install functions and a pointer to the entry is placed in yylval for future use.

Everything in the auxiliary function section is copied directly to lex.yy.c. Unlike declarations enclosed in %{ %}, however, auxiliary functions may be used in the actions

### 3.5.3: Conflict Resolution in Lex

1. Match the longest possible prefix of the input.
2. If this prefix matches multiple patterns, choose the first.

The first rule makes  = one instead of two lexemes. The second rule makes if a keyword and not an id.

### 3.5.3a: Anger Management in Lex

Sorry.

Sometimes a sequence of characters is only considered a certain lexeme if the sequence is followed by specified other sequences. Here is a classic example. Fortran, PL/I, and some other languages do not have reserved words. In Fortran
IF(X)=3
is a legal assignment statement and the IF is an identifier. However,
IF(X.LT.Y)X=Y
is an if/then statement and IF is a keyword. Sometimes the lack of reserved words makes lexical disambiguation impossible, however, in this case the slash / operator of lex is sufficient to distinguish the two cases. Consider

```      IF / \(.*\){letter}
```

This only matches IF when it is followed by a ( some text a ) and a letter. The only FORTRAN statements that match this are the if/then shown above; so we have found a lexeme that matches the if token. However, the lexeme is just the IF and not the rest of the pattern. The slash tells lex to put the rest back into the input and match it for the next and subsequent tokens.

Homework: 1(a-c), 2, 3.

## 3.6: Finite Automata

The secret weapon used by lex et al to convert (compile) its input into a lexer.

Finite automata are like the graphs we saw in transition diagrams but they simply decide if a sentence (input string) is in the language (generated by our regular expression). That is, they are recognizers of the language.

There are two types of finite automata

1. Deterministic finite automata (DFA) have for each state (circle in the diagram) exactly one edge leading out for each symbol. So if you know the next symbol and the current state, the next state is determined. That is, the execution is deterministic; hence the name.
2. Nondeterministic finite automata (NFA) are the other kind. There are no restrictions on the edges leaving a state: there can be several with the same symbol as label and some edges can be labeled with ε. Thus there can be several possible next states from a given state and a current lookahead symbol.

Surprising Theorem: Both DFAs and NFAs are capable of recognizing the same languages, the regular languages, i.e., the languages generated by regular expressions (plus the automata can recognize the empty language).

#### What Does This Theorem Mean?

There are certainly NFAs that are not DFAs. But the language recognized by each such NFA can also be recognized by at least one DFA.

#### Why Mention (Confusing) NFAs?

The DFAs that recognizes the same language as an NFA might be significantly larger than the NFA.

The finite automaton that one constructs naturally from a regular expression is often an NFA.

### 3.6.1: Nondeterministic Finite Automata

Here is the formal definition.

A nondeterministic finite automaton (NFA) consists of
1. A finite set of states S.
2. An input alphabet Σ not containing ε.
3. A transition function that gives, for each state and each symbol in Σ ∪ ε, a set of next states (or successor states).
4. An element s0 of S, the start state.
5. A subset F of S, the accepting states (or final states).

An NFA is basically a flow chart like the transition diagrams we have already seen. Indeed an NFA (or a DFA, to be formally defined soon) can be represented by a transition graph whose nodes are states and whose edges are labeled with elements of Σ ∪ ε. The differences between a transition graph and our previous transition diagrams are:

1. Possibly multiple edges with the same label leaving a single state.
2. An edge may be labeled with ε.

The transition graph to the right is an NFA for the regular expression (a|b)*abb, which (given the alphabet {a,b}) represents all words ending in abb.

Consider aababb. If you choose the wrong edge for the initial a's you will get stuck. But an NFA accepts a word if any path (beginning at the start state and using the symbols in the word in order) ends at an accepting state.

It essentially tries all such paths at once and accepts if any end at an accepting state. This means in addition that, if some paths uses all the input and end in a non-accepting state, but at least one path uses all the input (doesn't get stuck) and ends in an accepting state, the input is accepted.

Remark: Patterns like (a|b)*abb are useful regular expressions! If the alphabet is ascii, consider *.java.

Homework: 3, 4.

### 3.6.2: Transition Tables

Stateabε
0{0,1}{0}φ
1φ{2}φ
2φ{3}φ

There is an equivalent way to represent an NFA, namely a table giving, for each state s and input symbol x (and ε), the set of successor states x leads to from s. The empty set φ is used when there is no edge labeled x emanating from s. The table on the right corresponds to the transition graph above.

The downside of these tables is their size, especially if most of the entries are φ since those entries would not take any space in a transition graph.

Homework: 5.

### 3.6.3: Acceptance of Input Strings by Automata

An NFA accepts a string if the symbols of the string specify a path from the start to an accepting state.

Note that these symbols may specify several paths, some of which lead to accepting states and some that don't. In such a case the NFA does accept the string; one successful path is enough.

Also note that if an edge is labeled ε, then it can be taken for free.

For the transition graph above any string can just sit at state 0 since every possible symbol (namely a or b) can go from state 0 back to state 0. So every string can lead to a non-accepting state, but that is not important since, if just one path with that string leads to an accepting state, the NFA accepts the string.

The language defined by an NFA or the language accepted by an NFA is the set of strings (a.k.a. words) accepted by the NFA.

So the NFA in the diagram above accepts the same language as the regular expression (a|b)*abb.

If A is an automaton (NFA or DFA) we use L(A) for the language accepted by A.

The diagram on the right illustrates an NFA accepting the language L(aa*|bb*). The path
0 → 3 → 4 → 4 → 4 → 4
shows that bbbb is accepted by the NFA.

Note how the ε that labels the edge 0 → 3 does not appear in the string bbbb since ε is the empty string.

### 3.6.4: Deterministic Finite Automata

There is something weird about an NFA if viewed as a model of computation. How is a computer of any realistic construction able to check out all the (possibly infinite number of) paths to determine if any terminate at an accepting state?

We now consider a much more realistic model, a DFA.

Definition: A deterministic finite automata or DFA is a special case of an NFA having the restrictions
1. No edge is labeled with ε
2. For any state s and symbol a, there is exactly one edge leaving s with label a. (If no edge is shown with label a, there is an implied edge leading to a node labeled fail.)

This is realistic. We are at a state and examine the next character in the string, depending on the character we go to exactly one new state. Looks like a switch statement to me.

Minor point: when we write a transition table for a DFA, the entries are elements not sets so there are no {} present.

#### Simulating a DFA

Indeed a DFA is so reasonable there is an obvious algorithm for simulating it (i.e., reading a string and deciding whether or not it is in the language accepted by the DFA). We present it now.

```    s := s0;   // start state.
c := nextChar();      // a priming read
while (c /= eof) {
s := move(s,c);
c := nextChar();
}
if (s is in F, the set of accepting states) return yes
else return no
```

## 3.7: From Regular Expressions to Automata

### 3.7.0: Not Losing Site of the Forest Due to the Trees

This is not from the book.

Do not forget the goal of the chapter is to understand lexical analysis. (Languages generated by) regular expressions are the key to this task, since we use them to specify tokens. So we want to recognize (languages generated by) regular expressions. We are going to see two methods.

1. Convert the regular expression to an NFA and simulate the NFA.
2. Convert the regular expression to an NFA, convert the NFA to a DFA, and simulate the DFA.

So we need to learn 4 techniques.

1. Convert a regular expression to an NFA
2. Simulate an NFA
3. Convert an NFA to a DFA
4. Simulate a DFA.

The list I just gave is in the order the algorithms would be applied—but you would use either 2 or (3 and 4).

However, we will follow the order in the book, which is exactly the reverse.

Indeed, we just did item #4 and will now do #3.

### 3.7.1: Converting an NFA to a DFA

The book gives a detailed proof; I am just trying to motivate the ideas.

Let N be an NFA, we construct D, a DFA that accepts the same strings as does N. Call a state of N an N-state, and call a state of D a D-state.

The idea is that a D-state corresponds to a set of N-states and hence the procedure we are describing is called the subset algorithm. Specifically for each string X of symbols we consider all the N-states that can result when N processes X. This set of N-states is a D-state. Let us consider the transition graph on the right, which is an NFA that accepts strings satisfying the regular expression
(a|b)*abb. The alphabet is {a,b}.

The start state of D is the set of N-states that can result when N processes the empty string ε. This is called the ε-closure of the start state s0 of N, and consists of those N-states that can be reached from s0 by following edges labeled with ε. Specifically it is the set {0,1,2,4,7} of N-states. We call this state D0 and enter it in the transition table we are building for D on the right.
NFA statesDFA stateab
{0,1,2,4,7}D0D1D2
{1,2,3,4,6,7,8}D1D1D3
{1,2,4,5,6,7}D2D1D2
{1,2,4,5,6,7,9}D3D1D4
{1,2,4,5,6,7,10}D4D1D2

Next we want the a-successor of D0, i.e., the D-state that occurs when we start at D0 and move along an edge labeled a. We call this successor D1. Since D0 consists of the N-states corresponding to ε, D1 is the N-states corresponding to εa=a. We compute the a-successor of all the N-states in D0 and then form the ε-closure.

Next we compute the b-successor of D0 the same way and call it D2.

We continue forming a- and b-successors of all the D-states until no new D-states result (there are only a finite number of subsets of all the N-states so this process does indeed stop).

This gives the table on the right. D4 is the only D-accepting state as it is the only D-state containing the (only) N-accepting state 10.

Theoretically, this algorithm is awful since for a set with k elements, there are 2k subsets. Fortunately, normally only a small fraction of the possible subsets occur in practice. For example, the NFA we just did had 11 states so there are 2048 possible subsets, only 5 of which occurred in the DFA we constructed.

Homework: 1.

### 3.7.2: Simulating an NFA

Instead of producing the DFA, we can run the subset algorithm as a simulation itself. This is item #2 in my list of techniques

```    S = ε-closure(s0);
c = nextChar();
while ( c != eof ) {
S = ε-closure(move(S,c));
c = nextChar();
}
if ( S ∩ F != φ ) return yes;   // F is accepting states
else return no;
```

Homework: 2.

### 3.7.3: Efficiency of NFA Simulation

Slick implementation.

### 3.7.4: Constructing an NFA from a Regular Expression

I give a pictorial proof by induction. This is item #1 from my list of techniques.

1. The base cases are the empty regular expression and the regular expression consisting of a single symbol a in the alphabet.
2. The inductive cases are.
1. s | t for s and t regular expressions
2. st for s and t regular expressions
3. s*
4. (s), which is trivial since the nfa for s also works for (s).
3. We have so few cases because regular expressions (w/o extensions) are sufficient. That is, once we can construct arbitrary REs, we can also construct the extended REs and the regular definitions since both of these can be expressed as REs.

The pictures on the right illustrate the base and inductive cases.

Remarks:

1. The generated NFA has at most twice as many states as there are operators and operands in the RE. This is important for studying the complexity of the NFA, which we will not do.
2. The generated NFA has one start and one accepting state. The accepting state has no outgoing arcs and the start state has no incoming arcs. This is important for the pictorial proof we gave since we always assumed that the constituent NFAs had that property when building the bigger NFS.
3. Note that the diagram for st correctly indicates that the final state of s and the initial state of t are merged. This is one use of the previous remark that there is only one start state and one final state.
4. Except for the accepting state, each state of the generated NFA has either one outgoing arc labeled with a symbol or two outgoing arcs labeled with ε.

Do the NFA for (a|b)*abb and see that we get the same diagram that we had before.

Do the steps in the normal leftmost, innermost order (or draw a normal parse tree and follow it).

Homework: 3 a,b,c

## 3.8: Design of a Lexical-Analyzer Generator

How lexer-generators like Lex work. Since your lab2 is to produce a lexer, this is also a section on how you should solve lab2.