(*<*)
theory Paper
imports "../ReStar" "~~/src/HOL/Library/LaTeXsugar"
begin

declare [[show_question_marks = false]]

abbreviation 
 "der_syn r c \<equiv> der c r"

abbreviation 
 "ders_syn r s \<equiv> ders s r"

notation (latex output)
  If  ("(\<^raw:\textrm{>if\<^raw:}> (_)/ \<^raw:\textrm{>then\<^raw:}> (_)/ \<^raw:\textrm{>else\<^raw:}> (_))" 10) and
  Cons ("_\<^raw:\mbox{$\,$}>::\<^raw:\mbox{$\,$}>_" [75,73] 73) and  

  ZERO ("\<^bold>0" 78) and 
  ONE ("\<^bold>1" 78) and 
  CHAR ("_" [1000] 80) and
  ALT ("_ + _" [77,77] 78) and
  SEQ ("_ \<cdot> _" [77,77] 78) and
  STAR ("_\<^sup>\<star>" [1000] 78) and
  
  val.Void ("'(')" 79) and
  val.Char ("Char _" [1000] 79) and
  val.Left ("Left _" [79] 78) and
  val.Right ("Right _" [79] 78) and
  val.Seq ("Seq _ _" [79,79] 78) and
  val.Stars ("Stars _" [79] 78) and

  L ("L'(_')" [10] 78) and
  der_syn ("_\\_" [79, 1000] 76) and  
  ders_syn ("_\\_" [79, 1000] 76) and
  flat ("|_|" [75] 74) and
  Sequ ("_ @ _" [78,77] 63) and
  injval ("inj _ _ _" [79,77,79] 76) and 
  mkeps ("mkeps _" [79] 76) and 
  (*projval ("proj _ _ _" [1000,77,1000] 77) and*) 
  length ("len _" [78] 73) and
  matcher ("lexer _ _" [78,78] 77) and

  Prf ("_ : _" [75,75] 75) and  
  PMatch ("'(_, _') \<rightarrow> _" [63,75,75] 75) and
  (* and ValOrd ("_ \<succeq>\<^bsub>_\<^esub> _" [78,77,77] 73) *)
  
  F_RIGHT ("F\<^bsub>Right\<^esub> _") and
  F_LEFT ("F\<^bsub>Left\<^esub> _") and  
  F_ALT ("F\<^bsub>Alt\<^esub> _ _") and
  F_SEQ1 ("F\<^bsub>Seq1\<^esub> _ _") and
  F_SEQ2 ("F\<^bsub>Seq2\<^esub> _ _") and
  F_SEQ ("F\<^bsub>Seq\<^esub> _ _") and
  simp_SEQ ("simp\<^bsub>Seq\<^esub> _ _" [1000, 1000] 1) and
  simp_ALT ("simp\<^bsub>Alt\<^esub> _ _" [1000, 1000] 1) and
  matcher3 ("lexer\<^sup>+ _ _")

definition 
  "match r s \<equiv> nullable (ders s r)"

(*>*)

section {* Introduction *}


text {*

Brzozowski \cite{Brzozowski1964} introduced the notion of the {\em
derivative} @{term "der c r"} of a regular expression @{text r} w.r.t.\ a
character~@{text c}, and showed that it gave a simple solution to the
problem of matching a string @{term s} with a regular expression @{term r}:
if the derivative of @{term r} w.r.t.\ (in succession) all the characters of
the string matches the empty string, then @{term r} matches @{term s} (and
{\em vice versa}). The derivative has the property (which may almost be
regarded as its specification) that, for every string @{term s} and regular
expression @{term r} and character @{term c}, one has @{term "cs \<in> L(r)"} if
and only if \mbox{@{term "s \<in> L(der c r)"}}. The beauty of Brzozowski's
derivatives is that they are neatly expressible in any functional language,
and easily definable and reasoned about in theorem provers---the definitions
just consist of inductive datatypes and simple recursive functions. A
completely formalised correctness proof of this matcher in for example HOL4
has been mentioned in~\cite{Owens2008}. Another one in Isabelle/HOL is part
of the work in \cite{Krauss2011}.

One limitation of Brzozowski's matcher is that it only generates a YES/NO
answer for whether a string is being matched by a regular expression.
Sulzmann and Lu \cite{Sulzmann2014} extended this matcher to allow
generation not just of a YES/NO answer but of an actual matching, called a
[lexical] {\em value}. They give a simple algorithm to calculate a value
that appears to be the value associated with POSIX matching
\cite{Kuklewicz,Vansummeren2006}. The challenge then is to specify that
value, in an algorithm-independent fashion, and to show that Sulzmann and
Lu's derivative-based algorithm does indeed calculate a value that is
correct according to the specification.

The answer given by Sulzmann and Lu \cite{Sulzmann2014} is to define a
relation (called an ``order relation'') on the set of values of @{term r},
and to show that (once a string to be matched is chosen) there is a maximum
element and that it is computed by their derivative-based algorithm. This
proof idea is inspired by work of Frisch and Cardelli \cite{Frisch2004} on a
GREEDY regular expression matching algorithm. Beginning with our
observations that, without evidence that it is transitive, it cannot be
called an ``order relation'', and that the relation is called a ``total
order'' despite being evidently not total\footnote{The relation @{text
"\<ge>\<^bsub>r\<^esub>"} defined in \cite{Sulzmann2014} is a relation on the
values for the regular expression @{term r}; but it only holds between
@{term v} and @{term "v'"} in cases where @{term v} and @{term "v'"} have
the same flattening (underlying string). So a counterexample to totality is
given by taking two values @{term v} and @{term "v'"} for @{term r} that
have different flattenings (see Section~\ref{posixsec}). A different
relation @{text "\<ge>\<^bsub>r,s\<^esub>"} on the set of values for @{term r}
with flattening @{term s} is definable by the same approach, and is indeed
total; but that is not what Proposition 1 of \cite{Sulzmann2014} does.}, we
identify problems with this approach (of which some of the proofs are not
published in \cite{Sulzmann2014}); perhaps more importantly, we give a
simple inductive (and algorithm-independent) definition of what we call
being a {\em POSIX value} for a regular expression @{term r} and a string
@{term s}; we show that the algorithm computes such a value and that such a
value is unique. Proofs are both done by hand and checked in Isabelle/HOL.
The experience of doing our proofs has been that this mechanical checking
was absolutely essential: this subject area has hidden snares. This was also
noted by Kuklewitz \cite{Kuklewicz} who found that nearly all POSIX matching
implementations are ``buggy'' \cite[Page 203]{Sulzmann2014}.

If a regular expression matches a string, then in general there is more than
one way of how the string is matched. There are two commonly used
disambiguation strategies to generate a unique answer: one is called GREEDY
matching \cite{Frisch2004} and the other is POSIX
matching~\cite{Kuklewicz,Sulzmann2014,Vansummeren2006}. For example consider
the string @{term xy} and the regular expression \mbox{@{term "STAR (ALT
(ALT x y) xy)"}}. Either the string can be matched in two `iterations' by
the single letter-regular expressions @{term x} and @{term y}, or directly
in one iteration by @{term xy}. The first case corresponds to GREEDY
matching, which first matches with the left-most symbol and only matches the
next symbol in case of a mismatch (this is greedy in the sense of preferring
instant gratification to delayed repletion). The second case is POSIX
matching, which prefers the longest match.

In the context of lexing, where an input string needs to be split up into a
sequence of tokens, POSIX is the more natural disambiguation strategy for
what programmers consider basic syntactic building blocks in their programs.
These building blocks are often specified by some regular expressions, say
@{text "r\<^bsub>key\<^esub>"} and @{text "r\<^bsub>id\<^esub>"} for recognising keywords and
identifiers, respectively. There are two underlying (informal) rules behind
tokenising a string in a POSIX fashion according to a collection of regular
expressions:

\begin{itemize} 
\item[$\bullet$] \underline{The Longest Match Rule (or ``maximal munch rule''):}

The longest initial substring matched by any regular expression is taken as
next token.\smallskip

\item[$\bullet$] \underline{Priority Rule:}

For a particular longest initial substring, the first regular expression
that can match determines the token.
\end{itemize}
 
\noindent Consider for example @{text "r\<^bsub>key\<^esub>"} recognising keywords such as
@{text "if"}, @{text "then"} and so on; and @{text "r\<^bsub>id\<^esub>"} recognising
identifiers (say, a single character followed by characters or numbers).
Then we can form the regular expression @{text "(r\<^bsub>key\<^esub> + r\<^bsub>id\<^esub>)\<^sup>\<star>"} and use
POSIX matching to tokenise strings, say @{text "iffoo"} and @{text "if"}.
For @{text "iffoo"} we obtain by the longest match rule a single identifier
token, not a keyword followed by an identifier. For @{text "if"} we obtain by
the priority rule a keyword token, not an identifier token---even if @{text
"r\<^bsub>id\<^esub>"} matches also.\bigskip

\noindent {\bf Contributions:} We have implemented in Isabelle/HOL the
derivative-based regular expression matching algorithm of
Sulzmann and Lu \cite{Sulzmann2014}. We have proved the correctness of this
algorithm according to our specification of what a POSIX value is. Sulzmann
and Lu sketch in \cite{Sulzmann2014} an informal correctness proof: but to
us it contains unfillable gaps.\footnote{An extended version of
\cite{Sulzmann2014} is available at the website of its first author; this
extended version already includes remarks in the appendix that their
informal proof contains gaps, and possible fixes are not fully worked out.}
Our specification of a POSIX value consists of a simple inductive definition
that given a string and a regular expression uniquely determines this value.
Derivatives as calculated by Brzozowski's method are usually more complex
regular expressions than the initial one; various optimisations are
possible. We prove the correctness when simplifications of @{term "ALT ZERO
r"}, @{term "ALT r ZERO"}, @{term "SEQ ONE r"} and @{term "SEQ r ONE"} to
@{term r} are applied.

*}

section {* Preliminaries *}

text {* \noindent Strings in Isabelle/HOL are lists of characters with the
empty string being represented by the empty list, written @{term "[]"}, and
list-cons being written as @{term "DUMMY # DUMMY"}. Often we use the usual
bracket notation for lists also for strings; for example a string consisting
of just a single character @{term c} is written @{term "[c]"}. By using the
type @{type char} for characters we have a supply of finitely many
characters roughly corresponding to the ASCII character set. Regular
expressions are defined as usual as the elements of the following inductive
datatype:

  \begin{center}
  @{text "r :="}
  @{const "ZERO"} $\mid$
  @{const "ONE"} $\mid$
  @{term "CHAR c"} $\mid$
  @{term "ALT r\<^sub>1 r\<^sub>2"} $\mid$
  @{term "SEQ r\<^sub>1 r\<^sub>2"} $\mid$
  @{term "STAR r"} 
  \end{center}

  \noindent where @{const ZERO} stands for the regular expression that does
  not match any string, @{const ONE} for the regular expression that matches
  only the empty string and @{term c} for matching a character literal. The
  language of a regular expression is also defined as usual by the
  recursive function @{term L} with the clauses:

  \begin{center}
  \begin{tabular}{l@ {\hspace{5mm}}rcl}
  (1) & @{thm (lhs) L.simps(1)} & $\dn$ & @{thm (rhs) L.simps(1)}\\
  (2) & @{thm (lhs) L.simps(2)} & $\dn$ & @{thm (rhs) L.simps(2)}\\
  (3) & @{thm (lhs) L.simps(3)} & $\dn$ & @{thm (rhs) L.simps(3)}\\
  (4) & @{thm (lhs) L.simps(4)[of "r\<^sub>1" "r\<^sub>2"]} & $\dn$ & @{thm (rhs) L.simps(4)[of "r\<^sub>1" "r\<^sub>2"]}\\
  (5) & @{thm (lhs) L.simps(5)[of "r\<^sub>1" "r\<^sub>2"]} & $\dn$ & @{thm (rhs) L.simps(5)[of "r\<^sub>1" "r\<^sub>2"]}\\
  (6) & @{thm (lhs) L.simps(6)} & $\dn$ & @{thm (rhs) L.simps(6)}\\
  \end{tabular}
  \end{center}
  
  \noindent In clause (4) we use the operation @{term "DUMMY ;;
  DUMMY"} for the concatenation of two languages (it is also list-append for
  strings). We use the star-notation for regular expressions and for
  languages (in the last clause above). The star for languages is defined
  inductively by two clauses: @{text "(i)"} the empty string being in
  the star of a language and @{text "(ii)"} if @{term "s\<^sub>1"} is in a
  language and @{term "s\<^sub>2"} in the star of this language, then also @{term
  "s\<^sub>1 @ s\<^sub>2"} is in the star of this language. It will also be convenient
  to use the following notion of a \emph{semantic derivative} (or \emph{left
  quotient}) of a language defined as:

  \begin{center}
  \begin{tabular}{lcl}
  @{thm (lhs) Der_def} & $\dn$ & @{thm (rhs) Der_def}\\
  \end{tabular}
  \end{center}
  
  \noindent 
  For semantic derivatives we have the following equations (for example
  mechanically proved in \cite{Krauss2011}):

  \begin{equation}\label{SemDer}
  \begin{array}{lcl}
  @{thm (lhs) Der_null}  & \dn & @{thm (rhs) Der_null}\\
  @{thm (lhs) Der_empty}  & \dn & @{thm (rhs) Der_empty}\\
  @{thm (lhs) Der_char}  & \dn & @{thm (rhs) Der_char}\\
  @{thm (lhs) Der_union}  & \dn & @{thm (rhs) Der_union}\\
  @{thm (lhs) Der_Sequ}  & \dn & @{thm (rhs) Der_Sequ}\\
  @{thm (lhs) Der_star}  & \dn & @{thm (rhs) Der_star}
  \end{array}
  \end{equation}


  \noindent \emph{\Brz's derivatives} of regular expressions
  \cite{Brzozowski1964} can be easily defined by two recursive functions:
  the first is from regular expressions to booleans (implementing a test
  when a regular expression can match the empty string), and the second
  takes a regular expression and a character to a (derivative) regular
  expression:

  \begin{center}
  \begin{tabular}{lcl}
  @{thm (lhs) nullable.simps(1)} & $\dn$ & @{thm (rhs) nullable.simps(1)}\\
  @{thm (lhs) nullable.simps(2)} & $\dn$ & @{thm (rhs) nullable.simps(2)}\\
  @{thm (lhs) nullable.simps(3)} & $\dn$ & @{thm (rhs) nullable.simps(3)}\\
  @{thm (lhs) nullable.simps(4)[of "r\<^sub>1" "r\<^sub>2"]} & $\dn$ & @{thm (rhs) nullable.simps(4)[of "r\<^sub>1" "r\<^sub>2"]}\\
  @{thm (lhs) nullable.simps(5)[of "r\<^sub>1" "r\<^sub>2"]} & $\dn$ & @{thm (rhs) nullable.simps(5)[of "r\<^sub>1" "r\<^sub>2"]}\\
  @{thm (lhs) nullable.simps(6)} & $\dn$ & @{thm (rhs) nullable.simps(6)}\medskip\\
  \end{tabular}
  \end{center}

  \begin{center}
  \begin{tabular}{lcl}
  @{thm (lhs) der.simps(1)} & $\dn$ & @{thm (rhs) der.simps(1)}\\
  @{thm (lhs) der.simps(2)} & $\dn$ & @{thm (rhs) der.simps(2)}\\
  @{thm (lhs) der.simps(3)} & $\dn$ & @{thm (rhs) der.simps(3)}\\
  @{thm (lhs) der.simps(4)[of c "r\<^sub>1" "r\<^sub>2"]} & $\dn$ & @{thm (rhs) der.simps(4)[of c "r\<^sub>1" "r\<^sub>2"]}\\
  @{thm (lhs) der.simps(5)[of c "r\<^sub>1" "r\<^sub>2"]} & $\dn$ & @{thm (rhs) der.simps(5)[of c "r\<^sub>1" "r\<^sub>2"]}\\
  @{thm (lhs) der.simps(6)} & $\dn$ & @{thm (rhs) der.simps(6)}
  \end{tabular}
  \end{center}
 
  \noindent
  We may extend this definition to give derivatives w.r.t.~strings:

  \begin{center}
  \begin{tabular}{lcl}
  @{thm (lhs) ders.simps(1)} & $\dn$ & @{thm (rhs) ders.simps(1)}\\
  @{thm (lhs) ders.simps(2)} & $\dn$ & @{thm (rhs) ders.simps(2)}\\
  \end{tabular}
  \end{center}

  \noindent Given the equations in \eqref{SemDer}, it is a relatively easy
  exercise in mechanical reasoning to establish that

  \begin{proposition}\label{derprop}\mbox{}\\ 
  \begin{tabular}{ll}
  @{text "(1)"} & @{thm (lhs) nullable_correctness} if and only if
  @{thm (rhs) nullable_correctness}, and \\ 
  @{text "(2)"} & @{thm[mode=IfThen] der_correctness}.
  \end{tabular}
  \end{proposition}

  \noindent With this in place it is also very routine to prove that the
  regular expression matcher defined as

  \begin{center}
  @{thm match_def}
  \end{center}

  \noindent gives a positive answer if and only if @{term "s \<in> L r"}.
  Consequently, this regular expression matching algorithm satisfies the
  usual specification for regular expression matching. While the matcher
  above calculates a provably correct YES/NO answer for whether a regular
  expression matches a string or not, the novel idea of Sulzmann and Lu
  \cite{Sulzmann2014} is to append another phase to this algorithm in order
  to calculate a [lexical] value. We will explain the details next.

*}

section {* POSIX Regular Expression Matching\label{posixsec} *}

text {* 

  The clever idea in \cite{Sulzmann2014} is to introduce values for encoding
  \emph{how} a regular expression matches a string and then define a
  function on values that mirrors (but inverts) the construction of the
  derivative on regular expressions. \emph{Values} are defined as the
  inductive datatype

  \begin{center}
  @{text "v :="}
  @{const "Void"} $\mid$
  @{term "val.Char c"} $\mid$
  @{term "Left v"} $\mid$
  @{term "Right v"} $\mid$
  @{term "Seq v\<^sub>1 v\<^sub>2"} $\mid$ 
  @{term "Stars vs"} 
  \end{center}  

  \noindent where we use @{term vs} to stand for a list of values. (This is
  similar to the approach taken by Frisch and Cardelli for GREEDY matching
  \cite{Frisch2004}, and Sulzmann and Lu \cite{Sulzmann2014} for POSIX
  matching). The string underlying a value can be calculated by the @{const
  flat} function, written @{term "flat DUMMY"} and defined as:

  \begin{center}
  \begin{tabular}{lcl}
  @{thm (lhs) flat.simps(1)} & $\dn$ & @{thm (rhs) flat.simps(1)}\\
  @{thm (lhs) flat.simps(2)} & $\dn$ & @{thm (rhs) flat.simps(2)}\\
  @{thm (lhs) flat.simps(3)} & $\dn$ & @{thm (rhs) flat.simps(3)}\\
  @{thm (lhs) flat.simps(4)} & $\dn$ & @{thm (rhs) flat.simps(4)}\\
  @{thm (lhs) flat.simps(5)[of "v\<^sub>1" "v\<^sub>2"]} & $\dn$ & @{thm (rhs) flat.simps(5)[of "v\<^sub>1" "v\<^sub>2"]}\\
  @{thm (lhs) flat.simps(6)} & $\dn$ & @{thm (rhs) flat.simps(6)}\\
  @{thm (lhs) flat.simps(7)} & $\dn$ & @{thm (rhs) flat.simps(7)}\\
  \end{tabular}
  \end{center}

  \noindent Sulzmann and Lu also define inductively an inhabitation relation
  that associates values to regular expressions:

  \begin{center}
  \begin{tabular}{c}
  @{thm[mode=Axiom] Prf.intros(4)} \qquad
  @{thm[mode=Axiom] Prf.intros(5)[of "c"]}\smallskip\\
  @{thm[mode=Rule] Prf.intros(2)[of "v\<^sub>1" "r\<^sub>1" "r\<^sub>2"]} \qquad 
  @{thm[mode=Rule] Prf.intros(3)[of "v\<^sub>2" "r\<^sub>1" "r\<^sub>2"]}\smallskip\\
  @{thm[mode=Rule] Prf.intros(1)[of "v\<^sub>1" "r\<^sub>1" "v\<^sub>2" "r\<^sub>2"]}\smallskip\\ 
  @{thm[mode=Axiom] Prf.intros(6)[of "r"]} \qquad  
  @{thm[mode=Rule] Prf.intros(7)[of "v" "r" "vs"]}
  \end{tabular}
  \end{center}

  \noindent Note that no values are associated with the regular expression
  @{term ZERO}, and that the only value associated with the regular
  expression @{term ONE} is @{term Void}, pronounced (if one must) as @{text
  "Void"}. It is routine to establish how values ``inhabiting'' a regular
  expression correspond to the language of a regular expression, namely

  \begin{proposition}
  @{thm L_flat_Prf}
  \end{proposition}

  In general there is more than one value associated with a regular
  expression. In case of POSIX matching the problem is to calculate the
  unique value that satisfies the (informal) POSIX rules from the
  Introduction. Graphically the POSIX value calculation algorithm by
  Sulzmann and Lu can be illustrated by the picture in Figure~\ref{Sulz}
  where the path from the left to the right involving @{term derivatives}/@{const
  nullable} is the first phase of the algorithm (calculating successive
  \Brz's derivatives) and @{const mkeps}/@{text inj}, the path from right to
  left, the second phase. This picture shows the steps required when a
  regular expression, say @{text "r\<^sub>1"}, matches the string @{term
  "[a,b,c]"}. We first build the three derivatives (according to @{term a},
  @{term b} and @{term c}). We then use @{const nullable} to find out
  whether the resulting derivative regular expression @{term "r\<^sub>4"}
  can match the empty string. If yes, we call the function @{const mkeps}
  that produces a value @{term "v\<^sub>4"} for how @{term "r\<^sub>4"} can
  match the empty string (taking into account the POSIX constraints in case
  there are several ways). This functions is defined by the clauses:

\begin{figure}[t]
\begin{center}
\begin{tikzpicture}[scale=2,node distance=1.3cm,
                    every node/.style={minimum size=7mm}]
\node (r1)  {@{term "r\<^sub>1"}};
\node (r2) [right=of r1]{@{term "r\<^sub>2"}};
\draw[->,line width=1mm](r1)--(r2) node[above,midway] {@{term "der a DUMMY"}};
\node (r3) [right=of r2]{@{term "r\<^sub>3"}};
\draw[->,line width=1mm](r2)--(r3) node[above,midway] {@{term "der b DUMMY"}};
\node (r4) [right=of r3]{@{term "r\<^sub>4"}};
\draw[->,line width=1mm](r3)--(r4) node[above,midway] {@{term "der c DUMMY"}};
\draw (r4) node[anchor=west] {\;\raisebox{3mm}{@{term nullable}}};
\node (v4) [below=of r4]{@{term "v\<^sub>4"}};
\draw[->,line width=1mm](r4) -- (v4);
\node (v3) [left=of v4] {@{term "v\<^sub>3"}};
\draw[->,line width=1mm](v4)--(v3) node[below,midway] {@{text "inj r\<^sub>3 c"}};
\node (v2) [left=of v3]{@{term "v\<^sub>2"}};
\draw[->,line width=1mm](v3)--(v2) node[below,midway] {@{text "inj r\<^sub>2 b"}};
\node (v1) [left=of v2] {@{term "v\<^sub>1"}};
\draw[->,line width=1mm](v2)--(v1) node[below,midway] {@{text "inj r\<^sub>1 a"}};
\draw (r4) node[anchor=north west] {\;\raisebox{-8mm}{@{term "mkeps"}}};
\end{tikzpicture}
\end{center}
\caption{The two phases of the algorithm by Sulzmann \& Lu \cite{Sulzmann2014},
matching the string @{term "[a,b,c]"}. The first phase (the arrows from 
left to right) is \Brz's matcher building successive derivatives. If the 
last regular expression is @{term nullable}, then the functions of the 
second phase are called (the top-down and right-to-left arrows): first 
@{term mkeps} calculates a value witnessing
how the empty string has been recognised by @{term "r\<^sub>4"}. After
that the function @{term inj} ``injects back'' the characters of the string into
the values.
\label{Sulz}}
\end{figure} 

  \begin{center}
  \begin{tabular}{lcl}
  @{thm (lhs) mkeps.simps(1)} & $\dn$ & @{thm (rhs) mkeps.simps(1)}\\
  @{thm (lhs) mkeps.simps(2)[of "r\<^sub>1" "r\<^sub>2"]} & $\dn$ & @{thm (rhs) mkeps.simps(2)[of "r\<^sub>1" "r\<^sub>2"]}\\
  @{thm (lhs) mkeps.simps(3)[of "r\<^sub>1" "r\<^sub>2"]} & $\dn$ & @{thm (rhs) mkeps.simps(3)[of "r\<^sub>1" "r\<^sub>2"]}\\
  @{thm (lhs) mkeps.simps(4)} & $\dn$ & @{thm (rhs) mkeps.simps(4)}\\
  \end{tabular}
  \end{center}

  \noindent Note that this function needs only to be partially defined,
  namely only for regular expressions that are nullable. In case @{const
  nullable} fails, the string @{term "[a,b,c]"} cannot be matched by @{term
  "r\<^sub>1"} and an error is raised instead. Note also how this function
  makes some subtle choices leading to a POSIX value: for example if an
  alternative regular expression, say @{term "ALT r\<^sub>1 r\<^sub>2"}, can
  match the empty string and furthermore @{term "r\<^sub>1"} can match the
  empty string, then we return a @{text Left}-value. The @{text
  Right}-value will only be returned if @{term "r\<^sub>1"} cannot match the empty
  string.

  The most interesting idea from Sulzmann and Lu \cite{Sulzmann2014} is
  the construction of a value for how @{term "r\<^sub>1"} can match the
  string @{term "[a,b,c]"} from the value how the last derivative, @{term
  "r\<^sub>4"} in Fig~\ref{Sulz}, can match the empty string. Sulzmann and
  Lu achieve this by stepwise ``injecting back'' the characters into the
  values thus inverting the operation of building derivatives, but on the level
  of values. The corresponding function, called @{term inj}, takes three
  arguments, a regular expression, a character and a value. For example in
  the first (or right-most) @{term inj}-step in Fig~\ref{Sulz} the regular
  expression @{term "r\<^sub>3"}, the character @{term c} from the last
  derivative step and @{term "v\<^sub>4"}, which is the value corresponding
  to the derivative regular expression @{term "r\<^sub>4"}. The result is
  the new value @{term "v\<^sub>3"}. The final result of the algorithm is
  the value @{term "v\<^sub>1"}. The @{term inj} function is defined by recursion on regular
  expressions and by analysing the shape of values (corresponding to 
  the derivative regular expressions).
  %
  \begin{center}
  \begin{tabular}{l@ {\hspace{5mm}}lcl}
  (1) & @{thm (lhs) injval.simps(1)} & $\dn$ & @{thm (rhs) injval.simps(1)}\\
  (2) & @{thm (lhs) injval.simps(2)[of "r\<^sub>1" "r\<^sub>2" "c" "v\<^sub>1"]} & $\dn$ & 
      @{thm (rhs) injval.simps(2)[of "r\<^sub>1" "r\<^sub>2" "c" "v\<^sub>1"]}\\
  (3) & @{thm (lhs) injval.simps(3)[of "r\<^sub>1" "r\<^sub>2" "c" "v\<^sub>2"]} & $\dn$ & 
      @{thm (rhs) injval.simps(3)[of "r\<^sub>1" "r\<^sub>2" "c" "v\<^sub>2"]}\\
  (4) & @{thm (lhs) injval.simps(4)[of "r\<^sub>1" "r\<^sub>2" "c" "v\<^sub>1" "v\<^sub>2"]} & $\dn$ 
      & @{thm (rhs) injval.simps(4)[of "r\<^sub>1" "r\<^sub>2" "c" "v\<^sub>1" "v\<^sub>2"]}\\
  (5) & @{thm (lhs) injval.simps(5)[of "r\<^sub>1" "r\<^sub>2" "c" "v\<^sub>1" "v\<^sub>2"]} & $\dn$ 
      & @{thm (rhs) injval.simps(5)[of "r\<^sub>1" "r\<^sub>2" "c" "v\<^sub>1" "v\<^sub>2"]}\\
  (6) & @{thm (lhs) injval.simps(6)[of "r\<^sub>1" "r\<^sub>2" "c" "v\<^sub>2"]} & $\dn$ 
      & @{thm (rhs) injval.simps(6)[of "r\<^sub>1" "r\<^sub>2" "c" "v\<^sub>2"]}\\
  (7) & @{thm (lhs) injval.simps(7)[of "r" "c" "v" "vs"]} & $\dn$ 
      & @{thm (rhs) injval.simps(7)[of "r" "c" "v" "vs"]}\\
  \end{tabular}
  \end{center}

  \noindent To better understand what is going on in this definition it
  might be instructive to look first at the three sequence cases (clauses
  (4)--(6)). In each case we need to construct an ``injected value'' for
  @{term "SEQ r\<^sub>1 r\<^sub>2"}. This must be a value of the form @{term
  "Seq DUMMY DUMMY"}. Recall the clause of the @{text derivative}-function
  for sequence regular expressions:

  \begin{center}
  @{thm (lhs) der.simps(5)[of c "r\<^sub>1" "r\<^sub>2"]} $\dn$ @{thm (rhs) der.simps(5)[of c "r\<^sub>1" "r\<^sub>2"]}
  \end{center}

  \noindent Consider first the @{text "else"}-branch where the derivative is @{term
  "SEQ (der c r\<^sub>1) r\<^sub>2"}. The corresponding value must therefore
  be the form @{term "Seq v\<^sub>1 v\<^sub>2"}, which matches the left-hand
  side in clause~(4) of @{term inj}. In the @{text "if"}-branch the derivative is an
  alternative, namely @{term "ALT (SEQ (der c r\<^sub>1) r\<^sub>2) (der c
  r\<^sub>2)"}. This means we either have to consider a @{text Left}- or
  @{text Right}-value. In case of the @{text Left}-value we know further it
  must be a value for a sequence regular expression. Therefore the pattern
  we match in the clause (5) is @{term "Left (Seq v\<^sub>1 v\<^sub>2)"},
  while in (6) it is just @{term "Right v\<^sub>2"}. One more interesting
  point is in the right-hand side of clause (6): since in this case the
  regular expression @{text "r\<^sub>1"} does not ``contribute'' to
  matching the string, that means it only matches the empty string, we need to
  call @{const mkeps} in order to construct a value for how @{term "r\<^sub>1"}
  can match this empty string. A similar argument applies for why we can
  expect in the left-hand side of clause (7) that the value is of the form
  @{term "Seq v (Stars vs)"}---the derivative of a star is @{term "SEQ (der c r)
  (STAR r)"}. Finally, the reason for why we can ignore the second argument
  in clause (1) of @{term inj} is that it will only ever be called in cases
  where @{term "c=d"}, but the usual linearity restrictions in patterns do
  not allow us to build this constraint explicitly into our function
  definition.\footnote{Sulzmann and Lu state this clause as @{thm (lhs)
  injval.simps(1)[of "c" "c"]} $\dn$ @{thm (rhs) injval.simps(1)[of "c"]},
  but our deviation is harmless.}

  The idea of the @{term inj}-function to ``inject'' a character, say
  @{term c}, into a value can be made precise by the first part of the
  following lemma, which shows that the underlying string of an injected
  value has a prepended character @{term c}; the second part shows that the
  underlying string of an @{const mkeps}-value is always the empty string
  (given the regular expression is nullable since otherwise @{text mkeps}
  might not be defined).

  \begin{lemma}\mbox{}\smallskip\\\label{Prf_injval_flat}
  \begin{tabular}{ll}
  (1) & @{thm[mode=IfThen] Prf_injval_flat}\\
  (2) & @{thm[mode=IfThen] mkeps_flat}
  \end{tabular}
  \end{lemma}

  \begin{proof}
  Both properties are by routine inductions: the first one can, for example,
  be proved by an induction over the definition of @{term derivatives}; the second by
  an induction on @{term r}. There are no interesting cases.\qed
  \end{proof}

  Having defined the @{const mkeps} and @{text inj} function we can extend
  \Brz's matcher so that a [lexical] value is constructed (assuming the
  regular expression matches the string). The clauses of the Sulzmann and Lu lexer are

  \begin{center}
  \begin{tabular}{lcl}
  @{thm (lhs) matcher.simps(1)} & $\dn$ & @{thm (rhs) matcher.simps(1)}\\
  @{thm (lhs) matcher.simps(2)} & $\dn$ & @{text "case"} @{term "matcher (der c r) s"} @{text of}\\
                     & & \phantom{$|$} @{term "None"}  @{text "\<Rightarrow>"} @{term None}\\
                     & & $|$ @{term "Some v"} @{text "\<Rightarrow>"} @{term "Some (injval r c v)"}                          
  \end{tabular}
  \end{center}

  \noindent If the regular expression does not match the string, @{const None} is
  returned, indicating an error is raised. If the regular expression \emph{does}
  match the string, then @{const Some} value is returned. One important
  virtue of this algorithm is that it can be implemented with ease in any
  functional programming language and also in Isabelle/HOL. In the remaining
  part of this section we prove that this algorithm is correct.

  The well-known idea of POSIX matching is informally defined by the longest
  match and priority rule (see Introduction); as correctly argued in \cite{Sulzmann2014}, this
  needs formal specification. Sulzmann and Lu define a \emph{dominance}
  relation\footnote{Sulzmann and Lu call it an ordering relation, but
  without giving evidence that it is transitive.} between values and argue
  that there is a maximum value, as given by the derivative-based algorithm.
  In contrast, we shall introduce a simple inductive definition that
  specifies directly what a \emph{POSIX value} is, incorporating the
  POSIX-specific choices into the side-conditions of our rules. Our
  definition is inspired by the matching relation given in
  \cite{Vansummeren2006}. The relation we define is ternary and written as
  \mbox{@{term "s \<in> r \<rightarrow> v"}}, relating strings, regular expressions and
  values.

  \begin{center}
  \begin{tabular}{c}
  @{thm[mode=Axiom] PMatch.intros(1)}@{text "P"}@{term "ONE"} \qquad
  @{thm[mode=Axiom] PMatch.intros(2)}@{text "P"}@{term "c"}\bigskip\\
  @{thm[mode=Rule] PMatch.intros(3)[of "s" "r\<^sub>1" "v" "r\<^sub>2"]}@{text "P+L"}\qquad
  @{thm[mode=Rule] PMatch.intros(4)[of "s" "r\<^sub>2" "v" "r\<^sub>1"]}@{text "P+R"}\bigskip\\
  $\mprset{flushleft}
   \inferrule
   {@{thm (prem 1) PMatch.intros(5)[of "s\<^sub>1" "r\<^sub>1" "v\<^sub>1" "s\<^sub>2" "r\<^sub>2" "v\<^sub>2"]} \qquad
    @{thm (prem 2) PMatch.intros(5)[of "s\<^sub>1" "r\<^sub>1" "v\<^sub>1" "s\<^sub>2" "r\<^sub>2" "v\<^sub>2"]} \\\\
    @{thm (prem 3) PMatch.intros(5)[of "s\<^sub>1" "r\<^sub>1" "v\<^sub>1" "s\<^sub>2" "r\<^sub>2" "v\<^sub>2"]}}
   {@{thm (concl) PMatch.intros(5)[of "s\<^sub>1" "r\<^sub>1" "v\<^sub>1" "s\<^sub>2" "r\<^sub>2" "v\<^sub>2"]}}$@{text "PS"}\\
  @{thm[mode=Axiom] PMatch.intros(7)}@{text "P[]"}\bigskip\\
  $\mprset{flushleft}
   \inferrule
   {@{thm (prem 1) PMatch.intros(6)[of "s\<^sub>1" "r" "v" "s\<^sub>2" "vs"]} \qquad
    @{thm (prem 2) PMatch.intros(6)[of "s\<^sub>1" "r" "v" "s\<^sub>2" "vs"]} \qquad
    @{thm (prem 3) PMatch.intros(6)[of "s\<^sub>1" "r" "v" "s\<^sub>2" "vs"]} \\\\
    @{thm (prem 4) PMatch.intros(6)[of "s\<^sub>1" "r" "v" "s\<^sub>2" "vs"]}}
   {@{thm (concl) PMatch.intros(6)[of "s\<^sub>1" "r" "v" "s\<^sub>2" "vs"]}}$@{text "P\<star>"}
  \end{tabular}
  \end{center}

  \noindent We claim that this relation captures the idea behind the two
  informal POSIX rules shown in the Introduction: Consider for example the
  rules @{text "P+L"} and @{text "P+R"} where the POSIX value for a string
  and an alternative regular expression, that is @{term "(s, ALT r\<^sub>1 r\<^sub>2)"},
  is specified---it is always a @{text "Left"}-value, \emph{except} when the
  string to be matched is not in the language of @{term "r\<^sub>1"}; only then it
  is a @{text Right}-value (see the side-condition in @{text "P+R"}).
  Interesting is also the rule for sequence regular expressions (@{text
  "PS"}). The first two premises state that @{term "v\<^sub>1"} and @{term "v\<^sub>2"}
  are the POSIX values for @{term "(s\<^sub>1, r\<^sub>1)"} and @{term "(s\<^sub>2, r\<^sub>2)"}
  respectively. Consider now the third premise and note that the POSIX value
  of this rule should match the string @{term "s\<^sub>1 @ s\<^sub>2"}. According to the
  longest match rule, we want that the @{term "s\<^sub>1"} is the longest initial
  split of \mbox{@{term "s\<^sub>1 @ s\<^sub>2"}} such that @{term "s\<^sub>2"} is still recognised
  by @{term "r\<^sub>2"}. Let us assume, contrary to the third premise, that there
  \emph{exist} an @{term "s\<^sub>3"} and @{term "s\<^sub>4"} such that @{term "s\<^sub>2"}
  can be split up into a non-empty string @{term "s\<^sub>3"} and a possibly empty
  string @{term "s\<^sub>4"}. Moreover the longer string @{term "s\<^sub>1 @ s\<^sub>3"} can be
  matched by @{text "r\<^sub>1"} and the shorter @{term "s\<^sub>4"} can still be
  matched by @{term "r\<^sub>2"}. In this case @{term "s\<^sub>1"} would \emph{not} be the
  longest initial split of \mbox{@{term "s\<^sub>1 @ s\<^sub>2"}} and therefore @{term "Seq v\<^sub>1
  v\<^sub>2"} cannot be a POSIX value for @{term "(s\<^sub>1 @ s\<^sub>2, SEQ r\<^sub>1 r\<^sub>2)"}. 
  The main point is that this side-condition ensures the longest 
  match rule is satisfied.

  A similar condition is imposed on the POSIX value in the @{text
  "P\<star>"}-rule. Also there we want that @{term "s\<^sub>1"} is the longest initial
  split of @{term "s\<^sub>1 @ s\<^sub>2"} and furthermore the corresponding value
  @{term v} cannot be flattened to the empty string. In effect, we require
  that in each ``iteration'' of the star, some non-empty substring needs to
  be ``chipped'' away; only in case of the empty string we accept @{term
  "Stars []"} as the POSIX value.

   We can prove that given a string @{term s} and regular expression @{term
   r}, the POSIX value @{term v} is uniquely determined by @{term "s \<in> r \<rightarrow>
   v"}.

  \begin{theorem}
  @{thm[mode=IfThen] PMatch_determ(1)[of _ _ "v\<^sub>1" "v\<^sub>2"]}
  \end{theorem}

  \begin{proof} By induction on the definition of @{term "s \<in> r \<rightarrow> v\<^sub>1"} and
  a case analysis of @{term "s \<in> r \<rightarrow> v\<^sub>2"}. This proof requires the
  auxiliary lemma that @{thm (prem 1) PMatch1(1)} implies @{thm (concl)
  PMatch1(1)} and @{thm (concl) PMatch1(2)}, which are both easily
  established by inductions.\qed \end{proof}

  \noindent
  Next is the lemma that shows the function @{term "mkeps"} calculates
  the POSIX value for the empty string and a nullable regular expression.

  \begin{lemma}\label{lemmkeps}
  @{thm[mode=IfThen] PMatch_mkeps}
  \end{lemma}

  \begin{proof}
  By routine induction on @{term r}.\qed 
  \end{proof}

  \noindent
  The central lemma for our POSIX relation is that the @{text inj}-function
  preserves POSIX values.

  \begin{lemma}\label{PMatch2}
  @{thm[mode=IfThen] PMatch2_roy_version}
  \end{lemma}

  \begin{proof}

  By induction on @{text r}. Suppose @{term "r = ALT r\<^sub>1 r\<^sub>2"}. There are
  two subcases, namely @{text "(a)"} \mbox{@{term "v = Left v'"}} and @{term
  "s \<in> der c r\<^sub>1 \<rightarrow> v'"}; and @{text "(b)"} @{term "v = Right v'"}, @{term
  "s \<notin> L (der c r\<^sub>1)"} and @{term "s \<in> der c r\<^sub>2 \<rightarrow> v'"}. In @{text "(a)"} we
  know @{term "s \<in> der c r\<^sub>1 \<rightarrow> v'"}, from which we can infer @{term "(c # s)
  \<in> r\<^sub>1 \<rightarrow> injval r\<^sub>1 c v'"} by induction hypothesis and hence @{term "(c #
  s) \<in> ALT r\<^sub>1 r\<^sub>2 \<rightarrow> injval (ALT r\<^sub>1 r\<^sub>2) c (Left v')"} as needed. Similarly
  in subcase @{text "(b)"} where, however, in addition we have to use
  Prop.~\ref{derprop}(2) in order to infer @{term "c # s \<notin> L r\<^sub>1"} from @{term
  "s \<notin> L (der c r\<^sub>1)"}.

  Suppose @{term "r = SEQ r\<^sub>1 r\<^sub>2"}. There are three subcases:
  
  \begin{quote}
  \begin{description}
  \item[@{text "(a)"}] @{term "v = Left (Seq v\<^sub>1 v\<^sub>2)"} and @{term "nullable r\<^sub>1"} 
  \item[@{text "(b)"}] @{term "v = Right v\<^sub>1"} and @{term "nullable r\<^sub>1"} 
  \item[@{text "(c)"}] @{term "v = Seq v\<^sub>1 v\<^sub>2"} and @{term "\<not> nullable r\<^sub>1"} 
  \end{description}
  \end{quote}

  \noindent For @{text "(a)"} we know @{term "s\<^sub>1 \<in> der c r\<^sub>1 \<rightarrow> v\<^sub>1"} and
  @{term "s\<^sub>2 \<in> r\<^sub>2 \<rightarrow> v\<^sub>2"} as well as
  
  \[@{term "\<not> (\<exists>s\<^sub>3 s\<^sub>4. s\<^sub>3 \<noteq> [] \<and> s\<^sub>3 @ s\<^sub>4 = s\<^sub>2 \<and> s\<^sub>1 @ s\<^sub>3 \<in> L (der c r\<^sub>1) \<and> s\<^sub>4 \<in> L r\<^sub>2)"}\]

  \noindent From the latter we can infer by Prop.~\ref{derprop}(2):

  \[@{term "\<not> (\<exists>s\<^sub>3 s\<^sub>4. s\<^sub>3 \<noteq> [] \<and> s\<^sub>3 @ s\<^sub>4 = s\<^sub>2 \<and> (c # s\<^sub>1) @ s\<^sub>3 \<in> L r\<^sub>1 \<and> s\<^sub>4 \<in> L r\<^sub>2)"}\]

  \noindent We can use the induction hypothesis for @{text "r\<^sub>1"} to obtain
  @{term "(c # s\<^sub>1) \<in> r\<^sub>1 \<rightarrow> injval r\<^sub>1 c v\<^sub>1"}. This all put together allows us to infer
  @{term "((c # s\<^sub>1) @ s\<^sub>2) \<in> SEQ r\<^sub>1 r\<^sub>2 \<rightarrow> Seq (injval r\<^sub>1 c v\<^sub>1) v\<^sub>2"}. The case @{text "(c)"}
  is similarly.

  For @{text "(b)"} we know @{term "s \<in> der c r\<^sub>2 \<rightarrow> v\<^sub>1"} and 
  @{term "s\<^sub>1 @ s\<^sub>2 \<notin> L (SEQ (der c r\<^sub>1) r\<^sub>2)"}. From the former
  we have @{term "(c # s) \<in> r\<^sub>2 \<rightarrow> (injval r\<^sub>2 c v\<^sub>1)"} by induction hypothesis
  for @{term "r\<^sub>2"}. From the latter we can infer

  \[@{term "\<not> (\<exists>s\<^sub>3 s\<^sub>4. s\<^sub>3 \<noteq> [] \<and> s\<^sub>3 @ s\<^sub>4 = c # s \<and> s\<^sub>3 \<in> L r\<^sub>1 \<and> s\<^sub>4 \<in> L r\<^sub>2)"}\]

  \noindent By Lem.~\ref{lemmkeps} we know @{term "[] \<in> r\<^sub>1 \<rightarrow> (mkeps r\<^sub>1)"}
  holds. Putting this all together, we can conclude with @{term "(c #
  s) \<in> SEQ r\<^sub>1 r\<^sub>2 \<rightarrow> Seq (mkeps r\<^sub>1) (injval r\<^sub>2 c v\<^sub>1)"}, as required.

  Finally suppose @{term "r = STAR r\<^sub>1"}. This case is very similar to the
  sequence case, except that we need to ensure that @{term "flat (injval r\<^sub>1
  c v\<^sub>1) \<noteq> []"}. This follows from @{term "(c # s\<^sub>1)
  \<in> r\<^sub>1 \<rightarrow> injval r\<^sub>1 c v\<^sub>1"}  (which in turn follows from @{term "s\<^sub>1 \<in> der c
  r\<^sub>1 \<rightarrow> v\<^sub>1"} and the induction hypothesis).\qed
  \end{proof}

  \noindent
  With Lem.~\ref{PMatch2} in place, it is completely routine to establish
  that the Sulzmann and Lu lexer satisfies our specification (returning
  an ``error'' iff the string is not in the language of the regular expression,
  and returning a unique POSIX value iff the string \emph{is} in the language):

  \begin{theorem}\mbox{}\smallskip\\
  \begin{tabular}{ll}
  (1) & @{thm (lhs) lex_correct1a} if and only if @{thm (rhs) lex_correct1a}\\
  (2) & @{thm (lhs) lex_correct3b} if and only if @{thm (rhs) lex_correct3b}\\
  \end{tabular}
  \end{theorem}

  \begin{proof}
  By induction on @{term s} using Lem.~\ref{lemmkeps} and \ref{PMatch2}.\qed  
  \end{proof}

  \noindent This concludes our correctness proof. Note that we have not
  changed the algorithm by Sulzmann and Lu,\footnote{Any deviation we introduced is harmless.} 
  but introduced our own
  specification for what a correct result---a POSIX value---should be.
  A strong point in favour of Sulzmann and Lu's algorithm is that it
  can be extended in various ways.

*}

section {* Extensions and Optimisations*}

text {*

  If we are interested in tokenising strings, then we need to not just
  split up the string into tokens, but also ``classify'' the tokens (for
  example whether it is a keyword or an identifier). This can be
  done with only minor modifications to the algorithm by introducing \emph{record regular
  expressions} and \emph{record values} (for example \cite{Sulzmann2014b}):

  \begin{center}  
  @{text "r :="}
  @{text "..."} $\mid$
  @{text "(l : r)"} \qquad\qquad
  @{text "v :="}
  @{text "..."} $\mid$
  @{text "(l : v)"}
  \end{center}
  
  \noindent where @{text l} is a label, say a string, @{text r} a regular
  expression and @{text v} a value. All functions can be smoothly extended
  to these regular expressions and values. For example \mbox{@{text "(l : r)"}} is
  nullable iff @{term r} is, and so on. The purpose of the record regular
  expression is to mark certain parts of a regular expression and then
  record in the calculated value which parts of the string were matched by
  this part. The label can then serve as classification of the tokens. For this recall the
  regular expression @{text "(r\<^bsub>key\<^esub> + r\<^bsub>id\<^esub>)\<^sup>\<star>"} for keywords and
  identifiers from the Introduction. With record regular expression we can
  form \mbox{@{text "((key : r\<^bsub>key\<^esub>) + (id : r\<^bsub>id\<^esub>))\<^sup>\<star>"}} and then traverse the
  calculated value and only collect the underlying strings in record values.
  With this we obtain finite sequences of pairs of labels and strings, for 
  example

  \[@{text "(l\<^sub>1 : s\<^sub>1), ..., (l\<^sub>n : s\<^sub>n)"}\]
  
  \noindent from which tokens with classifications (keyword-token,
  identifier-token and so on) can be extracted.

  Derivatives as calculated by \Brz's method are usually more complex
  regular expressions than the initial one; the result is that the matching
  and lexing algorithms are often abysmally slow. However, various
  optimisations are possible, such as the simplifications of @{term "ALT
  ZERO r"}, @{term "ALT r ZERO"}, @{term "SEQ ONE r"} and @{term "SEQ r
  ONE"} to @{term r}. These simplifications can speed up the algorithms considerably,
  as noted in \cite{Sulzmann2014}.
  One of the advantages of having a simple specification
  and correctness proof is that the latter can be refined to prove the 
  correctness of such simplification steps.

  While the simplification of regular expressions according to 
  rules like

  \begin{center}
  \begin{tabular}{lcl}
  @{term "ALT ZERO r"} & @{text "\<Rightarrow>"} & @{term r}\\
  @{term "ALT r ZERO"} & @{text "\<Rightarrow>"} & @{term r}\\
  @{term "SEQ ONE r"}  & @{text "\<Rightarrow>"} & @{term r}\\
  @{term "SEQ r ONE"}  & @{text "\<Rightarrow>"} & @{term r}
  \end{tabular}
  \end{center}

  \noindent is well understood, there is an obstacle with the POSIX value
  calculation algorithm by Sulzmann and Lu: if we build a derivative regular
  expression and then simplify it, we will calculate a POSIX value for this
  simplified derivative regular expression, \emph{not} for the original (unsimplified)
  derivative regular expression. Sulzmann and Lu overcome this obstacle by
  not just calculating a simplified regular expression, but also calculating
  a \emph{rectification function} that ``repairs'' the incorrect value.
  
  The rectification functions can be (slightly clumsily) implemented  in
  Isabelle/HOL as follows using some auxiliary functions:

  \begin{center}
  \begin{tabular}{lcl}
  @{thm (lhs) F_RIGHT.simps(1)} & $\dn$ & @{text "Right (f v)"}\\
  @{thm (lhs) F_LEFT.simps(1)} & $\dn$ & @{text "Left (f v)"}\\
  
  @{thm (lhs) F_ALT.simps(1)} & $\dn$ & @{text "Right (f\<^sub>2 v)"}\\
  @{thm (lhs) F_ALT.simps(2)} & $\dn$ & @{text "Left (f\<^sub>1 v)"}\\
  
  @{thm (lhs) F_SEQ1.simps(1)} & $\dn$ & @{text "Seq (f\<^sub>1 ()) (f\<^sub>2 v)"}\\
  @{thm (lhs) F_SEQ2.simps(1)} & $\dn$ & @{text "Seq (f\<^sub>1 v) (f\<^sub>2 ())"}\\
  @{thm (lhs) F_SEQ.simps(1)} & $\dn$ & @{text "Seq (f\<^sub>1 v\<^sub>1) (f\<^sub>2 v\<^sub>2)"}\bigskip\\

  @{term "simp_ALT (ZERO, DUMMY) (r\<^sub>2, f\<^sub>2)"} & $\dn$ & @{term "(r\<^sub>2, F_RIGHT f\<^sub>2)"}\\
  @{term "simp_ALT (r\<^sub>1, f\<^sub>1) (ZERO, DUMMY)"} & $\dn$ & @{term "(r\<^sub>1, F_LEFT f\<^sub>1)"}\\
  @{term "simp_ALT (r\<^sub>1, f\<^sub>1) (r\<^sub>2, f\<^sub>2)"} & $\dn$ & @{term "(ALT r\<^sub>1 r\<^sub>2, F_ALT f\<^sub>1 f\<^sub>2)"}\\
  @{term "simp_SEQ (ONE, f\<^sub>1) (r\<^sub>2, f\<^sub>2)"} & $\dn$ & @{term "(r\<^sub>2, F_SEQ1 f\<^sub>1 f\<^sub>2)"}\\
  @{term "simp_SEQ (r\<^sub>1, f\<^sub>1) (ONE, f\<^sub>2)"} & $\dn$ & @{term "(r\<^sub>1, F_SEQ2 f\<^sub>1 f\<^sub>2)"}\\
  @{term "simp_SEQ (r\<^sub>1, f\<^sub>1) (r\<^sub>2, f\<^sub>2)"} & $\dn$ & @{term "(SEQ r\<^sub>1 r\<^sub>2, F_SEQ f\<^sub>1 f\<^sub>2)"}\\
  \end{tabular}
  \end{center}

  \noindent
  The main simplification function is then 

  \begin{center}
  \begin{tabular}{lcl}
  @{term "simp (ALT r\<^sub>1 r\<^sub>2)"} & $\dn$ & @{term "simp_ALT (simp r\<^sub>1) (simp r\<^sub>2)"}\\
  @{term "simp (SEQ r\<^sub>1 r\<^sub>2)"} & $\dn$ & @{term "simp_SEQ (simp r\<^sub>1) (simp r\<^sub>2)"}\\
  @{term "simp r"} & $\dn$ & @{term "(r, id)"}\\
  \end{tabular}
  \end{center} 

  \noindent where @{term "id"} stands for the identity function. Note that
  we do not simplify under stars: this seems to slow down the algorithm,
  rather than speed up. The optimised lexer is then given by the clauses:
  
  \begin{center}
  \begin{tabular}{lcl}
  @{thm (lhs) matcher3.simps(1)} & $\dn$ & @{thm (rhs) matcher3.simps(1)}\\
  @{thm (lhs) matcher3.simps(2)} & $\dn$ & 
                         @{text "let (r\<^sub>s, f\<^sub>r) = simp (r "}$\backslash$@{text " c) in"}\\
                     & & @{text "case"} @{term "matcher3 r\<^sub>s s"} @{text of}\\
                     & & \phantom{$|$} @{term "None"}  @{text "\<Rightarrow>"} @{term None}\\
                     & & $|$ @{term "Some v"} @{text "\<Rightarrow>"} @{text "Some (inj r c (f\<^sub>r v))"}                          
  \end{tabular}
  \end{center}

  \noindent
  In the second clause we first calculate the derivative @{text "r \\ c"}
  and then simplify the result. This gives us a simplified derivative
  @{text "r\<^sub>s"} and a rectification function @{text "f\<^sub>r"}. The matcher
  is recursively called with the simplified derivative, but before
  we inject the character @{term c} into value, we need to rectify
  it (@{term "f\<^sub>r v"}). We can prove that

  \begin{lemma}
  @{term "matcher3 r s = matcher r s"}
  \end{lemma}

  \noindent
  holds but refer the reader to our mechanisation for details.

*}

section {* The Correctness Argument by Sulzmmann and Lu *}

text {*
  \newcommand{\greedy}{\succcurlyeq_{gr}}
  \newcommand{\posix}{>}

  An extended version of \cite{Sulzmann2014} is available at the website of
  its first author; this includes some ``proofs'', claimed in
  \cite{Sulzmann2014} to be ``rigorous''. Since these are evidently not in
  final form, we make no comment thereon, preferring to give general reasons
  for our belief that the approach of \cite{Sulzmann2014} is problematic.
  Their central definition is an ``ordering relation'' defined by the
  rules (slightly adapted to fit our notation):

\begin{center}  
\begin{tabular}{@ {}c@ {\hspace{2mm}}c@ {}}		 
$\infer{v_{1} \posix_{r_{1}} v'_{1}} 
       {Seq\,v_{1}\,v_{2} \posix_{r_{1}r_{2}} Seq\,v'_{1}\,v'_{2}}(C2)$
&
$\infer{v_{2} \posix_{r_{2}} v'_{2}} 
       {Seq\,v_{1}\,v_{2} \posix_{r_{1}r_{2}} Seq\,v_{1}\,v'_{2}}(C1)$
\medskip\\
		
		
$\infer{ len |v_{2}| > len |v_{1}|} 
       {Right \; v_{2} \posix_{r_{1}+r_{2}} Left \; v_{1}}(A1)$ 
&
$\infer{ len |v_{1}| \geq len |v_{2}|} 
       {Left \; v_{1} \posix_{r_{1}+r_{2}} Right \; v_{2}} (A2)$ 
\medskip\\	

$\infer{ v_{2} \posix_{r_{2}} v'_{2}} 
       {Right \; v_{2} \posix_{r_{1}+r_{2}} Right \; v'_{2}}(A3)$ & 

$\infer{ v_{1} \posix_{r_{1}} v'_{1}} 
       {Left \; v_{1} \posix_{r_{1}+r_{2}} Left \; v'_{1}}(A4)$ 
\medskip\\

 $\infer{|v :: vs| = []} {Stars\,[] \posix_{r^{\star}} Stars\,(v :: vs)}(K1)$ & 
 $\infer{|v :: vs| \neq []} {Stars\,(v :: vs) \posix_{r^{\star}} Stars\,[]}(K2)$
\medskip\\
	
$\infer{ v_{1} \posix_{r} v_{2}} 
       {Stars\,(v_{1} :: vs_{1}) \posix_{r^{\star}} Stars\,(v_{2} :: vs_{2})}(K3)$ & 
$\infer{ Stars\,vs_{1} \posix_{r^{\star}} Stars\,vs_{2}} 
       {Stars\,(v :: vs_{1}) \posix_{r^{\star}} Stars\,(v :: vs_{2})}(K4)$	
\end{tabular}
\end{center}

  \noindent
  The idea behind the rules $(A1)$ and $(A2)$ is that a $Left$-value is
  bigger than a $Right$-value, if the underlying string of the $Left$-value is
  bigger or equals to the underlying string of the $Right$-value. The order
  is reversed if the $Right$-value can match longer string than a $Left$-value.
  In this way the POSIX value is supposed to be the biggest value
  for a given string and regular expression.

  Sulzmann and Lu explicitly refer to the paper \cite{Frisch2004} by Frisch
  and Cardelli from where they have taken the idea for their correctness
  proof of the POSIX value algorithm. Frisch and Cardelli introduced a
  similar ordering for GREEDY matching and they show that their greedy
  matching algorithm always produces a maximal element according to this
  ordering (from all possible solutions). The only difference between their
  GREEDY ordering and the ``ordering'' by Sulzmann and Lu is that GREEDY, if
  possible, always prefers a $Left$-value over a $Right$-value, no matter
  what the underlying string is. This seems like only a very minor
  difference, but it leads to drastic consequences in terms what
  properties both orderings enjoy. What is interesting for our purposes is
  that the properties reflexivity, totality and transitivity for this GREEDY
  ordering can be proved relatively easily by induction.

  These properties of GREEDY, however, do not transfer to the POSIX
  ``ordering'' by Sulzmann and Lu. To start with, $v \geq_r v′$ is not
  defined inductively, but as $v = v′$ or $v >_r v′ \wedge |v| = |v′|$. This
  means that $v >_r v′$ does not imply $v \geq_r v′$. Moreover, transitivity
  does not hold in the ``usual'' formulation, for example:

\begin{proposition}
Suppose $v_1 : r$, $v_2 : r$ and $v_3 : r$.
If $v_1 \posix_r v_2$ and $v_2 \posix_r v_3$
then $v_1 \posix_r v_3$.
\end{proposition}

\noindent If formulated like this, then there are various counter examples:
FOr example let $r$ be $a + ((a + a)\cdot (a + \textbf{0}))$ then the $v_1$,
$v_2$ and $v_3$ below are values of $r$:

\begin{center}
\begin{tabular}{lcl}
 $v_1$ & $=$ & $Left(Char\;a)$\\
 $v_2$ & $=$ & $Right((Left(Char\;a), Right(Void)))$\\
 $v_3$ & $=$ & $Right((Right(Char\;a), Left(Char\;a)))$
\end{tabular}
\end{center}

\noindent Moreover $v_1 \posix_r v_2$ and $v_2 \posix_r v_3$,
but \emph{not} $v_1 \posix_r v_3$! The reason is that although
$v_3$ is a $Right$-value, it can match a longer string, namely
$|v_3| = aa$, while $|v_1|$ (and $|v_2|$) matches only $a$. So
transitivity in this formulation does not hold---in this
example actually $v_3 \posix_r v_1$!

Sulzmann and Lu ``fix'' this problem by weakening the
transitivity property. They require in addition that the
underlying strings are of the same length. This excludes the
counter example above and any counter-example we were able 
to find (by hand and by machine). Thus the transitivity 
lemma in should be formulated as:

\begin{property}
Suppose $v_1 : r$, $v_2 : r$ and 
$v_3 : r$, and also $|v_1|=|v_2|=|v_3|$.\\
If $v_1 \posix_r v_2$ and $v_2 \posix_r v_3$
then $v_1 \posix_r v_3$.
\end{property}

\noindent While we agree with Sulzmann and Lu that this property probably
holds, proving it seems not so straightforward: although one begins with the
assumption that the values have the same flattening, this
cannot be maintained as one descends into the induction. This is 
a problem that occurs in a number of places in their proof.
Sulzmann and Lu do not give an explicit proof
of the transitivity property, but give a closely related
property about the existence of maximal elements. They state
that this can be verified by an induction on $r$. We disagree
with this as we shall show next in case of transitivity.

The case where the reasoning breaks down is the sequence case,
say $r_1\cdot r_2$. The induction hypotheses in this case
are

\begin{center}
\begin{tabular}{@ {}cc@ {}}
\begin{tabular}{@ {}ll@ {}}
IH $r_1$:\\
$\forall v_1, v_2, v_3.$ 
  & $v_1 : r_1\;\wedge$\\
  & $v_2 : r_1\;\wedge$\\
  & $v_3 : r_1\;\wedge$\\
  & $|v_1|=|v_2|=|v_3|\;\wedge$\\
  & $v_1 \posix_{r_1} v_2\;\wedge\; v_2 \posix_{r_1} v_3$\medskip\\
  & $\;\;\Rightarrow v_1 \posix_{r_1} v_3$
\end{tabular} &
\begin{tabular}{@ {}ll@ {}}
IH $r_2$:\\
$\forall v_1, v_2, v_3.$ 
  & $v_1 : r_2\;\wedge$\\
  & $v_2 : r_2\;\wedge$\\
  & $v_3 : r_2\;\wedge$\\
  & $|v_1|=|v_2|=|v_3|\;\wedge$\\
  & $v_1 \posix_{r_2} v_2\;\wedge\; v_2 \posix_{r_2} v_3$\medskip\\
  & $\;\;\Rightarrow v_1 \posix_{r_2} v_3$
\end{tabular}
\end{tabular}
\end{center} 

\noindent
We can assume that 

\begin{equation}
Seq\,v_{1l}\, v_{1r} \posix_{r_1\cdot r_2} Seq\,v_{2l}\, v_{2r}
\qquad\textrm{and}\qquad
Seq\,v_{2l}\, v_{2r} \posix_{r_1\cdot r_2} Seq\,v_{3l}\, v_{3r}
\label{assms}
\end{equation}

\noindent
hold, and furthermore that the values have equal length, 
namely:

\begin{equation}
|(v_{1l}, v_{1r})| = |(v_{2l}, v_{2r})|
\qquad\textrm{and}\qquad
|(v_{2l}, v_{2r})| = |(v_{3l}, v_{3r})|
\label{lens}
\end{equation}

\noindent
We need to show that

\[
Seq\,v_{1l}\, v_{1r}) \posix_{r_1\cdot r_2} Seq\, v_{3l}\, v_{3r}
\]

\noindent holds. We can proceed by analysing how the
assumptions in \eqref{assms} have arisen. There are four
cases. Let us assume we are in the case where
we know 

\[
v_{1l} \posix_{r_1} v_{2l}
\qquad\textrm{and}\qquad
v_{2l} \posix_{r_1} v_{3l}
\]

\noindent and also know the corresponding inhabitation judgements.
This is exactly a case where we would like to apply the
induction hypothesis IH~$r_1$. But we cannot! We still need to
show that $|v_{1l}| = |v_{2l}|$ and $|v_{2l}| = |v_{3l}|$. We
know from \eqref{lens} that the lengths of the sequence values
are equal, but from this we cannot infer anything about the
lengths of the component values. Indeed in general they will
be unequal, that is 

\[
|v_{1l}| \not= |v_{2l}|  
\qquad\textrm{and}\qquad
|v_{1r}| \not= |v_{2r}|
\]

\noindent but still \eqref{lens} will hold. Now we are stuck,
since the IH does not apply. As said, this problem where the
induction hypothesis does not apply arises in several places in
the proof of Sulzmann and LU, not just when proving transitivity.

*}

section {* Conclusion *}

text {*

  We have implemented the POSIX value calculation algorithm introduced in
  \cite{Sulzmann2014}. Our implementation is nearly identical to the
  original and all modifications are harmless (like our char-clause for
  @{text inj}). We have proved this algorithm to be correct, but correct
  according to our own specification of what POSIX values are. Our
  specification (inspired from work in \cite{Vansummeren2006}) appears to be
  much simpler than in \cite{Sulzmann2014} and our proofs are nearly always
  straightforward. We have attempted to formalise the original proof
  by Sulzmann and Lu \cite{Sulzmann2014}, but we believe it contains
  unfillable gaps. In the online version of \cite{Sulzmann2014}, the authors
  already acknowledge some small problems, but our experience suggests
  there are more serious problems. 
  
  Closely related to our work is an automata-based lexer formalised by
  Nipkow \cite{Nipkow98}. This lexer also splits up strings into longest
  initial substrings, but Nipkow's algorithm is not completely
  computational. The algorithm by Sulzmann and Lu, in contrast, can be
  implemented easily in functional languages. A bespoke lexer for the
  Imp-Language is formalised in Coq as part of the Software Foundations book
  \cite{Pierce2015}. The disadvantage of such bespoke lexers is that they
  do not generalise easily to more advanced features.
  Our formalisation is available from
  \url{http://www.inf.kcl.ac.uk/staff/urbanc/lex}.

  %\noindent
  %{\bf Acknowledgements:}
  %We are grateful for the comments we received from anonymous
  %referees.
  \bibliographystyle{plain}
  \bibliography{root}

*}


(*<*)
end
(*>*)