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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}

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\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 does also

  answers with \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. We have already developed some

  simplification rules for this, but have not yet proved that they

  preserve the correctness of the algorithm. We also have not yet

  looked at extended regular expressions, such as bounded repetitions,

  negation and back-references.

\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 this is not the case---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 to do  much better than that---after all,

given a DFA and a string, deciding whether a string is matched by this

DFA should be linear in terms of the size of the regular expression and

the string?

Admittedly, the regular expression $(a^*)^*\,b$ is carefully chosen to

exhibit this super-linear behaviour.  But unfortunately, such regular

expressions are not just a few outliers. They are actually 

frequent enough to have a separate name 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}. Static analysis

approach that is both sound and complete exists\cite{17Bir}, but the running 

time on certain examples in the RegExLib and Snort regular expressions

libraries is unacceptable. Therefore the problem of efficiency still remains.

This superlinear blowup in matching algorithms sometimes causes

considerable grief 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{\url{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---causing web servers to grind 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. In this example, the time needed to process

the string was $O(n^2)$ with respect to the string length. This

quadratic overhead was enough for the homepage of Stack Exchange to

respond so slowly that the load balancer assumed there must be some

attack and therefore stopped the servers from responding to any

requests. This made the whole site become unavailable. Another very

recent example is a global outage of all Cloudflare servers on 2 July

2019. A poorly written regular expression exhibited exponential

behaviour and exhausted CPUs that serve HTTP traffic. Although the

outage had several causes, at the heart was a regular expression that

was used to monitor network

traffic.\footnote{\url{https://blog.cloudflare.com/details-of-the-cloudflare-outage-on-july-2-2019/}}

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 (for example how to treat efficiently bounded repetitions, negation and

back-references).

%question: dfa can have exponential states. isn't this the actual reason why they do not use dfas?

%how do they avoid dfas exponential states if they use them for fast matching?

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 also in \emph{how}.  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}. Such a

definition has also been given by Sulzmann and  Lu \cite{Sulzmann2014},

but the corresponding correctness proof turned out to be  faulty

\cite{AusafDyckhoffUrban2016}. Roughly, POSIX matching means matching

the longest initial substring. In the case of a tie, the initial

sub-match 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 in this project 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 this algorithm next.

\section{The Algorithm by Brzozowski based on Derivatives of Regular

Expressions}

Suppose (basic) regular expressions are given by the following grammar:

\[			r ::=   \ZERO \mid  \ONE

			 \mid  c  

			 \mid  r_1 \cdot r_2

			 \mid  r_1 + r_2   

			 \mid r^*         

\]

\noindent

The intended meaning of the constructors is as follows: $\ZERO$

cannot match any string, $\ONE$ can match the empty string, the

character regular expression $c$ can match the character $c$, and so

on.

The ingenious 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}

This function simply tests whether the empty string is in $L(r)$.

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}

%Assuming the classic notion of a

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

\noindent

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

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.

For the moment however, we focus only on the usual basic regular expressions.

Now if we want to find out whether a string $s$ matches with a regular

expression $r$, we can 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. To implement this idea

we can generalise the derivative operation to strings like this:

\begin{center}

\begin{tabular}{lcl}

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

$r \backslash [\,] $ & $\dn$ & $r$

\end{tabular}

\end{center}

\noindent

and then define as  regular-expression matching algorithm: 

\[

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

\]

\noindent

This algorithm looks graphically as follows:

\begin{equation}\label{graph:*}

\begin{tikzcd}

r_0 \arrow[r, "\backslash c_0"]  & r_1 \arrow[r, "\backslash c_1"] & r_2 \arrow[r, dashed]  & r_n  \arrow[r,"\textit{nullable}?"] & \;\textrm{YES}/\textrm{NO}

\end{tikzcd}

\end{equation}

\noindent

where we start with  a regular expression  $r_0$, build successive

derivatives until we exhaust the string and then use \textit{nullable}

to test whether the result can match the empty string. It can  be

relatively  easily shown that this matcher is correct  (that is given

an $s = c_0...c_{n-1}$ and an $r_0$, it generates YES if and only if $s \in L(r_0)$).

\section{Values and the Algorithm by Sulzmann and Lu}

One limitation 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} or

sometimes also \emph{lexical value}.  These values and regular

expressions correspond to each other as illustrated in the following

table:

\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

No value  corresponds to $\ZERO$; $\Empty$ corresponds to $\ONE$;

$\Char$ to the character regular expression; $\Seq$ to the sequence

regular expression and so on. The idea of values is to encode a kind of

lexical value for how the sub-parts of a regular expression match the

sub-parts of a string. To see this, suppose a \emph{flatten} operation,

written $|v|$ for values. We can use this function to extract the

underlying string of a value $v$. For example, $|\mathit{Seq} \,

(\textit{Char x}) \, (\textit{Char y})|$ is the string $xy$.  Using

flatten, we can describe how values encode lexical values: $\Seq\,v_1\,

v_2$ encodes a tree with two children nodes that tells how the string

$|v_1| @ |v_2|$ matches the regex $r_1 \cdot r_2$ whereby $r_1$ matches

the substring $|v_1|$ and, respectively, $r_2$ matches the substring

$|v_2|$. Exactly how these two are matched is contained in the children

nodes $v_1$ and $v_2$ of parent $\textit{Seq}$. 

To give a concrete example of how values work, 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 $\Stars \; [\ldots]$ records all the

iterations; 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---see \eqref{graph:*}). In this second phase, a POSIX value 

is generated in case the regular expression matches  the string. 

Pictorially, the Sulzmann and Lu algorithm is as follows:

\begin{ceqn}

\begin{equation}\label{graph:2}

\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}

\end{equation}

\end{ceqn}

\noindent

For convenience, we shall employ the following notations: the regular

expression we start with is $r_0$, and the given string $s$ is composed

of characters $c_0 c_1 \ldots c_{n-1}$. In  the first phase from the

left to right, we build the derivatives $r_1$, $r_2$, \ldots  according

to the characters $c_0$, $c_1$  until we exhaust the string and obtain

the derivative $r_n$. We test whether this derivative is

$\textit{nullable}$ or not. If not, we know the string does not match

$r$ and no value needs to be generated. If yes, we start building the

values incrementally by \emph{injecting} back the characters into the

earlier values $v_n, \ldots, v_0$. This is the second phase of the

algorithm from the right to left. For the first value $v_n$, we call the

function $\textit{mkeps}$, which builds the lexical value

for how the empty string has been matched by the (nullable) regular

expression $r_n$. This function is defined as

	\begin{center}

		\begin{tabular}{lcl}

			$\mkeps(\ONE)$ 		& $\dn$ & $\Empty$ \\

			$\mkeps(r_{1}+r_{2})$	& $\dn$ 

			& \textit{if} $\nullable(r_{1})$\\ 

			& & \textit{then} $\Left(\mkeps(r_{1}))$\\