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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 (for

the moment ignore the grammar for values on the right-hand side):

\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

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}

		$\ZERO \backslash c$ & $\dn$ & $\ZERO$\\  

		$\ONE \backslash c$  & $\dn$ & $\ZERO$\\

		$d \backslash c$     & $\dn$ & 

		$\mathit{if} \;c = d\;\mathit{then}\;\ONE\;\mathit{else}\;\ZERO$\\

$(r_1 + r_2)\backslash c$     & $\dn$ & $r_1 \backslash c \,+\, r_2 \backslash c$\\

$(r_1 \cdot r_2)\backslash c$ & $\dn$ & $\mathit{if} \nullable(r_1)$\\

	&   & $\mathit{then}\;(r_1\backslash c) \cdot r_2 \,+\, r_2\backslash c$\\

	&   & $\mathit{else}\;(r_1\backslash c) \cdot r_2$\\

	$(r^*)\backslash c$           & $\dn$ & $(r\backslash c) \cdot r^*$\\

\end{tabular}

\end{center}

\noindent

In this definition $\nullable(\_)$ stands for a simple recursive

function that tests whether a regular expression can match the empty

string (its definition is omitted). Assuming the classic notion of a

\emph{language} of a regular expression, written $L(\_)$, 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

The beauty of derivatives is that they lead to a really simple regular

expression matching algorithm: 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. 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}---see the grammar above for its definition.  Assuming a

regular expression matches a string, values encode the information of

\emph{how} the string is matched by the regular expression---that is,

which part of the string is matched by which part of the regular

expression. To illustrate this 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. As mentioned before, from this

value we can extract the information \emph{how} a regular expression

matched a string. We omit the details here on how Sulzmann and Lu

achieved this~(see \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. There are operations that can transform the

usual (un-annotated) regular expressions into annotated regular

expressions, and there are operations for encoding/decoding values to

or from bitsequences. For example the encoding function for values is

defined as follows:

\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} 

\noindent

where $\Z$ and $\S$ are arbitrary names for the ``bits'' in the

bitsequences. Although this encoding is ``lossy'' in the sense of not

recording all details of a value, Sulzmann and Lu have defined the

decoding function such that with additional information (namely the

corresponding regular expression) a value can be precisely extracted

from a bitsequence.

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 prunned 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}