\documentclass[a4paper,UKenglish]{lipics}

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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}

\algnewcommand\algorithmicswitch{\textbf{switch}}

\algnewcommand\algorithmiccase{\textbf{case}}

\algnewcommand\algorithmicassert{\texttt{assert}}

\algnewcommand\Assert[1]{\State \algorithmicassert(#1)}%

% New "environments"

\algdef{SE}[SWITCH]{Switch}{EndSwitch}[1]{\algorithmicswitch\ #1\ \algorithmicdo}{\algorithmicend\ \algorithmicswitch}%

\algdef{SE}[CASE]{Case}{EndCase}[1]{\algorithmiccase\ #1}{\algorithmicend\ \algorithmiccase}%

\algtext*{EndSwitch}%

\algtext*{EndCase}%

\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

\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.

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}

\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 started with the definition of $nullable$:

\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}

He then 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

 %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

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.

\begin{center}

\begin{tabular}{lcl}

$r \backslash (c\!::\!s) $ & $\dn$ & $(r \backslash c) \backslash s$ \\

$r \backslash \epsilon $ & $\dn$ & $r$

\end{tabular}

\end{center}

\noindent

Using the above definition we obtain a simple and elegant regular

expression matching algorithm: 

\[

match\;s\;r \;\dn\; nullable(r\backslash s)

\]

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. 

 The flatten notation $| v |$ means extracting the characters in the value $v$ to form a string. For example, $|\mathit{Seq} \, \mathit{Char(c)} \, \mathit{Char(d)}|$ = $cd$. We omit this straightforward definition.

 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 $v_2$. 

 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

\begin{tikzcd}

r_0 \arrow[r, "\backslash c_0"]  \arrow[d] & r_1 \arrow[r, "\backslash 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}

\begin{tikzcd}

r_0 \arrow[r, "\backslash c_0"]  & r_1 \arrow[r, "\backslash c_1"] & r_2 \arrow[r, dashed]  & r_n   

\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 how the empty word matched the empty regular expression 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}.

Antimirov defined the "partial derivatives" of regular expressions to be this:

%TODO definition of partial derivatives

it is essentially a set of regular expressions that come from the sub-structure of the original regular expression. 

Antimirov has proved a nice size bound of the size of partial derivatives. Roughly speaking the size will not exceed the fourth power of the number of nodes in that regular expression.  Interestingly, we observed from experiment that after the simplification step, our regular expression has the same size or is smaller than the partial derivatives. This allows us to prove a tight bound on the size of regular expression during the running time of the algorithm if we can establish the connection between our simplification rules and partial derivatives.

 %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:\\

%\begin{definition}[Bitdecoding of Values]\mbox{}

\begin{center}

\begin{tabular}{@{}l@{\hspace{1mm}}c@{\hspace{1mm}}l@{}}

  $\textit{decode}'\,bs\,(\ONE)$ & $\dn$ & $(\Empty, bs)$\\

  $\textit{decode}'\,bs\,(c)$ & $\dn$ & $(\Char\,c, bs)$\\

  $\textit{decode}'\,(\Z\!::\!bs)\;(r_1 + r_2)$ & $\dn$ &

     $\textit{let}\,(v, bs_1) = \textit{decode}'\,bs\,r_1\;\textit{in}\;

       (\Left\,v, bs_1)$\\

  $\textit{decode}'\,(\S\!::\!bs)\;(r_1 + r_2)$ & $\dn$ &

     $\textit{let}\,(v, bs_1) = \textit{decode}'\,bs\,r_2\;\textit{in}\;

       (\Right\,v, bs_1)$\\