\documentclass[a4paper,UKenglish]{lipics}
\usepackage{graphic}
\usepackage{data}
\usepackage{tikz-cd}
% \documentclass{article}
%\usepackage[utf8]{inputenc}
%\usepackage[english]{babel}
%\usepackage{listings}
% \usepackage{amsthm}
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\title{POSIX Regular Expression Matching and Lexing}
\author{Chengsong Tan}
\affil{King's College London\\
London, UK\\
\texttt{chengsong.tan@kcl.ac.uk}}
\authorrunning{Chengsong Tan}
\Copyright{Chengsong Tan}

\newcommand{\dn}{\stackrel{\mbox{\scriptsize def}}{=}}%
\newcommand{\ZERO}{\mbox{\bf 0}}
\newcommand{\ONE}{\mbox{\bf 1}}
\def\lexer{\mathit{lexer}}
\def\mkeps{\mathit{mkeps}}
\def\inj{\mathit{inj}}
\def\Empty{\mathit{Empty}}
\def\Left{\mathit{Left}}
\def\Right{\mathit{Right}}
\def\Stars{\mathit{Stars}}
\def\Char{\mathit{Char}}
\def\Seq{\mathit{Seq}}
\def\Der{\mathit{Der}}
\def\nullable{\mathit{nullable}}
\def\Z{\mathit{Z}}
\def\S{\mathit{S}}

%\theoremstyle{theorem}
%\newtheorem{theorem}{Theorem}
%\theoremstyle{lemma}
%\newtheorem{lemma}{Lemma}
%\newcommand{\lemmaautorefname}{Lemma}
%\theoremstyle{definition}
%\newtheorem{definition}{Definition}


\begin{document}

\maketitle

\begin{abstract}
  Brzozowski introduced in 1964 a beautifully simple algorithm for
  regular expression matching based on the notion of derivatives of
  regular expressions. In 2014, Sulzmann and Lu extended this
  algorithm to not just give a YES/NO answer for whether or not a regular
  expression matches a string, but in case it matches also \emph{how}
  it matches the string.  This is important for applications such as
  lexing (tokenising a string). The problem is to make the algorithm
  by Sulzmann and Lu fast on all inputs without breaking its
  correctness.
\end{abstract}

\section{Introduction}

This PhD-project is about regular expression matching and
lexing. Given the maturity of this topic, the reader might wonder:
Surely, regular expressions must have already been studied to death?
What could possibly be \emph{not} known in this area? And surely all
implemented algorithms for regular expression matching are blindingly
fast?



Unfortunately these preconceptions are not supported by evidence: Take
for example the regular expression $(a^*)^*\,b$ and ask whether
strings of the form $aa..a$ match this regular
expression. Obviously they do not match---the expected $b$ in the last
position is missing. One would expect that modern regular expression
matching engines can find this out very quickly. Alas, if one tries
this example in JavaScript, Python or Java 8 with strings like 28
$a$'s, one discovers that this decision takes around 30 seconds and
takes considerably longer when adding a few more $a$'s, as the graphs
below show:

\begin{center}
\begin{tabular}{@{}c@{\hspace{0mm}}c@{\hspace{0mm}}c@{}}
\begin{tikzpicture}
\begin{axis}[
    xlabel={$n$},
    x label style={at={(1.05,-0.05)}},
    ylabel={time in secs},
    enlargelimits=false,
    xtick={0,5,...,30},
    xmax=33,
    ymax=35,
    ytick={0,5,...,30},
    scaled ticks=false,
    axis lines=left,
    width=5cm,
    height=4cm, 
    legend entries={JavaScript},  
    legend pos=north west,
    legend cell align=left]
\addplot[red,mark=*, mark options={fill=white}] table {re-js.data};
\end{axis}
\end{tikzpicture}
  &
\begin{tikzpicture}
\begin{axis}[
    xlabel={$n$},
    x label style={at={(1.05,-0.05)}},
    %ylabel={time in secs},
    enlargelimits=false,
    xtick={0,5,...,30},
    xmax=33,
    ymax=35,
    ytick={0,5,...,30},
    scaled ticks=false,
    axis lines=left,
    width=5cm,
    height=4cm, 
    legend entries={Python},  
    legend pos=north west,
    legend cell align=left]
\addplot[blue,mark=*, mark options={fill=white}] table {re-python2.data};
\end{axis}
\end{tikzpicture}
  &
\begin{tikzpicture}
\begin{axis}[
    xlabel={$n$},
    x label style={at={(1.05,-0.05)}},
    %ylabel={time in secs},
    enlargelimits=false,
    xtick={0,5,...,30},
    xmax=33,
    ymax=35,
    ytick={0,5,...,30},
    scaled ticks=false,
    axis lines=left,
    width=5cm,
    height=4cm, 
    legend entries={Java 8},  
    legend pos=north west,
    legend cell align=left]
\addplot[cyan,mark=*, mark options={fill=white}] table {re-java.data};
\end{axis}
\end{tikzpicture}\\
\multicolumn{3}{c}{Graphs: Runtime for matching $(a^*)^*\,b$ with strings 
           of the form $\underbrace{aa..a}_{n}$.}
\end{tabular}    
\end{center}  

\noindent These are clearly abysmal and possibly surprising results.
One would expect these systems doing much better than that---after
all, given a DFA and a string, whether a string is matched by this DFA
should be linear.

Admittedly, the regular expression $(a^*)^*\,b$ is carefully chosen to
exhibit this ``exponential behaviour''.  Unfortunately, such regular
expressions are not just a few ``outliers'', but actually they are
frequent enough that a separate name has been created for
them---\emph{evil regular expressions}. In empiric work, Davis et al
report that they have found thousands of such evil regular expressions
in the JavaScript and Python ecosystems \cite{Davis18}.

This exponential blowup sometimes causes real pain in real life:
for example on 20 July 2016 one evil regular expression brought the
webpage \href{http://stackexchange.com}{Stack Exchange} to its knees \footnote{https://stackstatus.net/post/147710624694/outage-postmortem-july-20-2016}.
In this instance, a regular expression intended to just trim white
spaces from the beginning and the end of a line actually consumed
massive amounts of CPU-resources and because of this the web servers
ground to a halt. This happened when a post with 20,000 white spaces
was submitted, but importantly the white spaces were neither at the
beginning nor at the end. As a result, the regular expression matching
engine needed to backtrack over many choices.

The underlying problem is that many ``real life'' regular expression
matching engines do not use DFAs for matching. This is because they
support regular expressions that are not covered by the classical
automata theory, and in this more general setting there are quite a
few research questions still unanswered and fast algorithms still need
to be developed.

There is also another under-researched problem to do with regular
expressions and lexing, i.e.~the process of breaking up strings into
sequences of tokens according to some regular expressions. In this
setting one is not just interested in whether or not a regular
expression matches a string, but if it matches also in \emph{how} it
matches the string.  Consider for example a regular expression
$r_{key}$ for recognising keywords such as \textit{if}, \textit{then}
and so on; and a regular expression $r_{id}$ for recognising
identifiers (say, a single character followed by characters or
numbers). One can then form the compound regular expression
$(r_{key} + r_{id})^*$ and use it to tokenise strings.  But then how
should the string \textit{iffoo} be tokenised?  It could be tokenised
as a keyword followed by an identifier, or the entire string as a
single identifier.  Similarly, how should the string \textit{if} be
tokenised? Both regular expressions, $r_{key}$ and $r_{id}$, would
``fire''---so is it an identifier or a keyword?  While in applications
there is a well-known strategy to decide these questions, called POSIX
matching, only relatively recently precise definitions of what POSIX
matching actually means have been formalised
\cite{AusafDyckhoffUrban2016,OkuiSuzuki2010,Vansummeren2006}. Roughly,
POSIX matching means matching the longest initial substring and
in the case of a tie, the initial submatch is chosen according to some priorities attached to the
regular expressions (e.g.~keywords have a higher priority than
identifiers). This sounds rather simple, but according to Grathwohl et
al \cite[Page 36]{CrashCourse2014} this is not the case. They wrote:

\begin{quote}
\it{}``The POSIX strategy is more complicated than the greedy because of 
the dependence on information about the length of matched strings in the 
various subexpressions.''
\end{quote}

\noindent
This is also supported by evidence collected by Kuklewicz
\cite{Kuklewicz} who noticed that a number of POSIX regular expression
matchers calculate incorrect results.

Our focus is on an algorithm introduced by Sulzmann and Lu in 2014 for
regular expression matching according to the POSIX strategy
\cite{Sulzmann2014}. Their algorithm is based on an older algorithm by
Brzozowski from 1964 where he introduced the notion of derivatives of
regular expressions \cite{Brzozowski1964}. We shall briefly explain
the algorithms next.

\section{The Algorithms by  Brzozowski, and Sulzmann and Lu}

Suppose regular expressions are given by the following grammar:


\begin{center}

		\begin{tabular}{@{}rrl@{}}
			\multicolumn{3}{@{}l}{\textbf{Regular Expressions}}\medskip\\
			$r$ & $::=$  & $\ZERO$\\
			& $\mid$ & $\ONE$   \\
			& $\mid$ & $c$          \\
			& $\mid$ & $r_1 \cdot r_2$\\
			& $\mid$ & $r_1 + r_2$   \\
			\\
			& $\mid$ & $r^*$         \\
		\end{tabular}

\end{center}

\noindent
The intended meaning of the regular expressions is as usual: $\ZERO$
cannot match any string, $\ONE$ can match the empty string, the
character regular expression $c$ can match the character $c$, and so
on. The brilliant contribution by Brzozowski is the notion of
\emph{derivatives} of regular expressions.  The idea behind this
notion is as follows: suppose a regular expression $r$ can match a
string of the form $c\!::\! s$ (that is a list of characters starting
with $c$), what does the regular expression look like that can match
just $s$? Brzozowski gave a neat answer to this question. He defined
the following operation on regular expressions, written
$r\backslash c$ (the derivative of $r$ w.r.t.~the character $c$):

\begin{center}
\begin{tabular}{lcl}
		$\ZERO \backslash c$ & $\dn$ & $\ZERO$\\  
		$\ONE \backslash c$  & $\dn$ & $\ZERO$\\
		$d \backslash c$     & $\dn$ & 
		$\mathit{if} \;c = d\;\mathit{then}\;\ONE\;\mathit{else}\;\ZERO$\\
$(r_1 + r_2)\backslash c$     & $\dn$ & $r_1 \backslash c \,+\, r_2 \backslash c$\\
$(r_1 \cdot r_2)\backslash c$ & $\dn$ & $\mathit{if} \, \epsilon \in L(r_1)$\\
	&   & $\mathit{then}\;(r_1\backslash c) \cdot r_2 \,+\, r_2\backslash c$\\
	&   & $\mathit{else}\;(r_1\backslash c) \cdot r_2$\\
	$(r^*)\backslash c$           & $\dn$ & $(r\backslash c) \cdot r^*$\\
\end{tabular}
\end{center}

\noindent
The $\mathit{if}$ condition in the definition of $(r_1 \cdot r_2) \backslash c$ involves a membership testing: $\epsilon \overset{?}{\in} L(r_1)$.
Such testing is easily implemented by the following simple recursive function $\nullable(\_)$:


\begin{center}
		\begin{tabular}{lcl}
			$\nullable(\ZERO)$     & $\dn$ & $\mathit{false}$ \\  
			$\nullable(\ONE)$      & $\dn$ & $\mathit{true}$ \\
			$\nullable(c)$ 	       & $\dn$ & $\mathit{false}$ \\
			$\nullable(r_1 + r_2)$ & $\dn$ & $\nullable(r_1) \vee \nullable(r_2)$ \\
			$\nullable(r_1\cdot r_2)$  & $\dn$ & $\nullable(r_1) \wedge \nullable(r_2)$ \\
			$\nullable(r^*)$       & $\dn$ & $\mathit{true}$ \\
		\end{tabular}
	\end{center}

 %Assuming the classic notion of a
%\emph{language} of a regular expression, written $L(\_)$, t
The main
property of the derivative operation is that

\begin{center}
$c\!::\!s \in L(r)$ holds
if and only if $s \in L(r\backslash c)$.
\end{center}

\noindent
So if we want to find out whether a string $s$
matches with a regular expression $r$, build the derivatives of $r$
w.r.t.\ (in succession) all the characters of the string $s$. Finally,
test whether the resulting regular expression can match the empty
string.  If yes, then $r$ matches $s$, and no in the negative
case.\\
If we define the successive derivative operation to be like this:
\begin{center}
\begin{tabular}{lcl}
$r \backslash (c\!::\!s) $ & $\dn$ & $(r \backslash c) \backslash s$ \\
$r \backslash \epsilon $ & $\dn$ & $r$
\end{tabular}
\end{center}


We obtain a simple and elegant regular
expression matching algorithm: 
\begin{definition}{matcher}
\[
match\;s\;r \;\dn\; nullable(r\backslash s)
\]
\end{definition}

 For us the main advantage is that derivatives can be
straightforwardly implemented in any functional programming language,
and are easily definable and reasoned about in theorem provers---the
definitions just consist of inductive datatypes and simple recursive
functions. Moreover, the notion of derivatives can be easily
generalised to cover extended regular expression constructors such as
the not-regular expression, written $\neg\,r$, or bounded repetitions
(for example $r^{\{n\}}$ and $r^{\{n..m\}}$), which cannot be so
straightforwardly realised within the classic automata approach.


One limitation, however, of Brzozowski's algorithm is that it only
produces a YES/NO answer for whether a string is being matched by a
regular expression.  Sulzmann and Lu~\cite{Sulzmann2014} extended this
algorithm to allow generation of an actual matching, called a
\emph{value}.  

\begin{center}
	\begin{tabular}{c@{\hspace{20mm}}c}
		\begin{tabular}{@{}rrl@{}}
			\multicolumn{3}{@{}l}{\textbf{Regular Expressions}}\medskip\\
			$r$ & $::=$  & $\ZERO$\\
			& $\mid$ & $\ONE$   \\
			& $\mid$ & $c$          \\
			& $\mid$ & $r_1 \cdot r_2$\\
			& $\mid$ & $r_1 + r_2$   \\
			\\
			& $\mid$ & $r^*$         \\
		\end{tabular}
		&
		\begin{tabular}{@{\hspace{0mm}}rrl@{}}
			\multicolumn{3}{@{}l}{\textbf{Values}}\medskip\\
			$v$ & $::=$  & \\
			&        & $\Empty$   \\
			& $\mid$ & $\Char(c)$          \\
			& $\mid$ & $\Seq\,v_1\, v_2$\\
			& $\mid$ & $\Left(v)$   \\
			& $\mid$ & $\Right(v)$  \\
			& $\mid$ & $\Stars\,[v_1,\ldots\,v_n]$ \\
		\end{tabular}
	\end{tabular}
\end{center}

\noindent
 Here we put the regular expression and values of the same shape on the same level to illustrate the corresponding relation between them. \\
 Values are a way of expressing parse trees(the tree structure that tells how a sub-regex matches a substring). For example, $\Seq\,v_1\, v_2$ tells us how the string $|v_1| \cdot |v_2|$ matches the regex $r_1 \cdot r_2$: $r_1$ matches $|v_1|$ and $r_2$ matches $|v_2|$. Exactly how these two are matched are contained in the sub-structure of $v_1$ and $v_2$. The flatten notation $| v |$ means extracting the characters in the value $v$ to form a string. For example, $|\mathit{Seq} \, \mathit{Char(c)} \, \mathit{Char(d)}|$ = $cd$.\\

 To give a concrete example of how value works, consider the string $xy$ and the
regular expression $(x + (y + xy))^*$. We can view this regular
expression as a tree and if the string $xy$ is matched by two Star
``iterations'', then the $x$ is matched by the left-most alternative
in this tree and the $y$ by the right-left alternative. This suggests
to record this matching as

\begin{center}
$\Stars\,[\Left\,(\Char\,x), \Right(\Left(\Char\,y))]$
\end{center}

\noindent
where $\Stars$ records how many
iterations were used; and $\Left$, respectively $\Right$, which
alternative is used. The value for
matching $xy$ in a single ``iteration'', i.e.~the POSIX value,
would look as follows

\begin{center}
$\Stars\,[\Seq\,(\Char\,x)\,(\Char\,y)]$
\end{center}

\noindent
where $\Stars$ has only a single-element list for the single iteration
and $\Seq$ indicates that $xy$ is matched by a sequence regular
expression.

The contribution of Sulzmann and Lu is an extension of Brzozowski's
algorithm by a second phase (the first phase being building successive
derivatives). In this second phase, for every successful match the
corresponding POSIX value is computed. The whole process looks like this diagram:\\
\begin{tikzcd}
r_0 \arrow[r, "c_0"]  \arrow[d] & r_1 \arrow[r, "c_1"] \arrow[d] & r_2 \arrow[r, dashed] \arrow[d] & r_n \arrow[d, "mkeps" description] \\
v_0           & v_1 \arrow[l,"inj_{r_0} c_0"]                & v_2 \arrow[l, "inj_{r_1} c_1"]              & v_n \arrow[l, dashed]         
\end{tikzcd}
We shall briefly explain this interesting process.\\ For the convenience of explanation, we have the following notations: the regular expression $r$ used for matching is also called $r_0$ and the string $s$ is composed of $n$ characters $c_0 c_1 ... c_{n-1}$.
First, we do the derivative operation on $r_0$, $r_1$, ..., using characters $c_0$, $c_1$, ...  until we get the final derivative $r_n$.We test whether it is $nullable$ or not. If no we know immediately the string does not match the regex. If yes, we start building the parse tree incrementally. We first call $mkeps$(which stands for make epsilon--make the parse tree for the empty word epsilon) to construct the parse tree $v_n$ for how the final derivative $r_n$ matches the empty string:

 After this, we inject back the characters one by one, in reverse order as they were chopped off, to build the parse tree $v_i$ for how the regex $r_i$ matches the string $s_i$($s_i$ means the string s with the first $i$ characters being chopped off) from the previous parse tree. After $n$ transformations, we get the parse tree for how $r_0$ matches $s$, exactly as we wanted.
An inductive proof can be routinely established.
We omit the details of injection function, which is provided by Sulzmann and Lu's paper \cite{Sulzmann2014}. Rather, we shall focus next on the
process of simplification of regular expressions, which is needed in
order to obtain \emph{fast} versions of the Brzozowski's, and Sulzmann
and Lu's algorithms.  This is where the PhD-project hopes to advance
the state-of-the-art.


\section{Simplification of Regular Expressions}

The main drawback of building successive derivatives according to
Brzozowski's definition is that they can grow very quickly in size.
This is mainly due to the fact that the derivative operation generates
often ``useless'' $\ZERO$s and $\ONE$s in derivatives.  As a result,
if implemented naively both algorithms by Brzozowski and by Sulzmann
and Lu are excruciatingly slow. For example when starting with the
regular expression $(a + aa)^*$ and building 12 successive derivatives
w.r.t.~the character $a$, one obtains a derivative regular expression
with more than 8000 nodes (when viewed as a tree). Operations like
derivative and $\nullable$ need to traverse such trees and
consequently the bigger the size of the derivative the slower the
algorithm. Fortunately, one can simplify regular expressions after
each derivative step. Various simplifications of regular expressions
are possible, such as the simplifications of $\ZERO + r$,
$r + \ZERO$, $\ONE\cdot r$, $r \cdot \ONE$, and $r + r$ to just
$r$. These simplifications do not affect the answer for whether a
regular expression matches a string or not, but fortunately also do
not affect the POSIX strategy of how regular expressions match
strings---although the latter is much harder to establish. Some
initial results in this regard have been obtained in
\cite{AusafDyckhoffUrban2016}. However, what has not been achieved yet
is a very tight bound for the size. Such a tight bound is suggested by
work of Antimirov who proved that (partial) derivatives can be bound
by the number of characters contained in the initial regular
expression \cite{Antimirov95}. We believe, and have generated test
data, that a similar bound can be obtained for the derivatives in
Sulzmann and Lu's algorithm. Let us give some details about this next.

We first followed Sulzmann and Lu's idea of introducing
\emph{annotated regular expressions}~\cite{Sulzmann2014}. They are
defined by the following grammar:

\begin{center}
\begin{tabular}{lcl}
  $\textit{a}$ & $::=$  & $\textit{ZERO}$\\
                  & $\mid$ & $\textit{ONE}\;\;bs$\\
                  & $\mid$ & $\textit{CHAR}\;\;bs\,c$\\
                  & $\mid$ & $\textit{ALTS}\;\;bs\,as$\\
                  & $\mid$ & $\textit{SEQ}\;\;bs\,a_1\,a_2$\\
                  & $\mid$ & $\textit{STAR}\;\;bs\,a$
\end{tabular}    
\end{center}  

\noindent
where $bs$ stands for bitsequences, and $as$ (in \textit{ALTS}) for a
list of annotated regular expressions. These bitsequences encode
information about the (POSIX) value that should be generated by the
Sulzmann and Lu algorithm. Bitcodes are essentially incomplete values.
This can be straightforwardly seen in the following transformation: 
\begin{center}
\begin{tabular}{lcl}
  $\textit{code}(\Empty)$ & $\dn$ & $[]$\\
  $\textit{code}(\Char\,c)$ & $\dn$ & $[]$\\
  $\textit{code}(\Left\,v)$ & $\dn$ & $\Z :: code(v)$\\
  $\textit{code}(\Right\,v)$ & $\dn$ & $\S :: code(v)$\\
  $\textit{code}(\Seq\,v_1\,v_2)$ & $\dn$ & $code(v_1) \,@\, code(v_2)$\\
  $\textit{code}(\Stars\,[])$ & $\dn$ & $[\S]$\\
  $\textit{code}(\Stars\,(v\!::\!vs))$ & $\dn$ & $\Z :: code(v) \;@\;
                                                 code(\Stars\,vs)$
\end{tabular}    
\end{center} 
where $\Z$ and $\S$ are arbitrary names for the bits in the
bitsequences. 
Here code encodes a value into a bitsequence by converting Left into $\Z$, Right into $\S$, the start point of a non-empty star iteration into $\S$, and the border where a local star terminates into $\Z$. This conversion is apparently lossy, as it throws away the character information, and does not decode the boundary between the two operands of the sequence constructor. Moreover, with only the bitcode we cannot even tell whether the $\S$s and $\Z$s are for $Left/Right$ or $Stars$. The reason for choosing this compact way of storing information is that the relatively small size of bits can be easily moved around during the lexing process. In order to recover the bitcode back into values, we will need the regular expression as the extra information and decode them back into value:\\
TODO: definition of decode
\\

To do lexing using annotated regular expressions, we shall first transform the
usual (un-annotated) regular expressions into annotated regular
expressions:\\
TODO: definition of internalise
\\
Then we do successive derivative operations on the annotated regular expression. This derivative operation is the same as what we previously have for the simple regular expressions, except that we take special care of the bits to store the parse tree information:\\
TODO: bder
\\
This way, we do not have to use an injection function and a second phase, but instead only need to collect the bits while running $mkeps$:
TODO: mkepsBC
\\
and then decode the bits using the regular expression. The whole process looks like this:\\
r
\\

The main point of the bitsequences and annotated regular expressions
is that we can apply rather aggressive (in terms of size)
simplification rules in order to keep derivatives small.  

We have
developed such ``aggressive'' simplification rules and generated test
data that show that the expected bound can be achieved. Obviously we
could only partially cover  the search space as there are infinitely
many regular expressions and strings. One modification we introduced
is to allow a list of annotated regular expressions in the
\textit{ALTS} constructor. This allows us to not just delete
unnecessary $\ZERO$s and $\ONE$s from regular expressions, but also
unnecessary ``copies'' of regular expressions (very similar to
simplifying $r + r$ to just $r$, but in a more general
setting). Another modification is that we use simplification rules
inspired by Antimirov's work on partial derivatives. They maintain the
idea that only the first ``copy'' of a regular expression in an
alternative contributes to the calculation of a POSIX value. All
subsequent copies can be pruned from the regular expression.

We are currently engaged with proving that our simplification rules
actually do not affect the POSIX value that should be generated by the
algorithm according to the specification of a POSIX value and
furthermore that our derivatives stay small for all derivatives. For
this proof we use the theorem prover Isabelle. Once completed, this
result will advance the state-of-the-art: Sulzmann and Lu wrote in
their paper \cite{Sulzmann2014} about the bitcoded ``incremental
parsing method'' (that is the matching algorithm outlined in this
section):

\begin{quote}\it
  ``Correctness Claim: We further claim that the incremental parsing
  method in Figure~5 in combination with the simplification steps in
  Figure 6 yields POSIX parse trees. We have tested this claim
  extensively by using the method in Figure~3 as a reference but yet
  have to work out all proof details.''
\end{quote}  

\noindent
We would settle the correctness claim and furthermore obtain a much
tighter bound on the sizes of derivatives. The result is that our
algorithm should be correct and faster on all inputs.  The original
blow-up, as observed in JavaScript, Python and Java, would be excluded
from happening in our algorithm.

\section{Conclusion}

In this PhD-project we are interested in fast algorithms for regular
expression matching. While this seems to be a ``settled'' area, in
fact interesting research questions are popping up as soon as one steps
outside the classic automata theory (for example in terms of what kind
of regular expressions are supported). The reason why it is
interesting for us to look at the derivative approach introduced by
Brzozowski for regular expression matching, and then much further
developed by Sulzmann and Lu, is that derivatives can elegantly deal
with some of the regular expressions that are of interest in ``real
life''. This includes the not-regular expression, written $\neg\,r$
(that is all strings that are not recognised by $r$), but also bounded
regular expressions such as $r^{\{n\}}$ and $r^{\{n..m\}}$). There is
also hope that the derivatives can provide another angle for how to
deal more efficiently with back-references, which are one of the
reasons why regular expression engines in JavaScript, Python and Java
choose to not implement the classic automata approach of transforming
regular expressions into NFAs and then DFAs---because we simply do not
know how such back-references can be represented by DFAs.


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\end{document}