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%%https://jex.im/regulex/

%%https://www.reddit.com/r/ProgrammingLanguages/comments/42dlem/mona_compiler_development_part_2_parsing/

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%% email validator

%% http://www.ex-parrot.com/%7Epdw/Mail-RFC822-Address.html

% https://jackfoxy.github.io/FsRegEx/emailregex.html

%% regex testers

% https://regex101.com

% http://regexr.com

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%% https://www.gnu.org/software/emacs/manual/html_node/elisp/Regular-Expressions.html

%%  reasons for a new programming language

%% http://beautifulracket.com

% compiler explorer

% https://gcc.godbolt.org

% good article how languages have been influenced

% 10 MOST(LY DEAD) INFLUENTIAL PROGRAMMING LANGUAGES

% https://www.hillelwayne.com/post/influential-dead-languages/

\begin{document}

\fnote{\copyright{} Christian Urban, King's College London, 2014, 2015, 2016, 2017, 2018, 2019, 2020}

\section*{Handout 1}

The purpose of a compiler is to transform a program a human can read and

write into code the machine can run as fast as possible. Developing a

compiler is an old craft going back to 1952 with the first compiler

written by Grace Hopper.\footnote{Who many years ago was invited on a

talk show hosted by David Letterman.

\here{https://youtu.be/3N_ywhx6_K0?t=31}} Why studying compilers

nowadays?  An interesting answer is given by John Regher in his compiler

blog:\here{http://blog.regehr.org/archives/1419}

\begin{quote}\it{}``We can start off with a couple of observations

  about the role of compilers. First, hardware is getting weirder

  rather than getting clocked faster: almost all processors are

  multicores and it looks like there is increasing asymmetry in

  resources across cores. Processors come with vector units, crypto

  accelerators, bit twiddling instructions, and lots of features to

  make virtualization and concurrency work. We have DSPs, GPUs,

  big.little, and Xeon Phi. This is only scratching the

  surface. Second, we’re getting tired of low-level languages and

  their associated security disasters, we want to write new code, to

  whatever extent possible, in safer, higher-level

  languages. Compilers are caught right in the middle of these

  opposing trends: one of their main jobs is to help bridge the large

  and growing gap between increasingly high-level languages and

  increasingly wacky platforms. It’s effectively a perpetual

  employment act for solid compiler hackers.''

\end{quote}  

\noindent

So the goal of this module is to become a solid (beginner) compiler

hacker and as part of the coursework to implement a small

compiler for a very small programming language.

The first part of the module is about the problem of text processing,

which is needed for compilers, but also for dictionaries, DNA-data,

spam-filters and so on. When looking for a particular string, say

\pcode{"foobar"}, in a large text we can use the Knuth-Morris-Pratt

algorithm, which is currently the most efficient general string search

algorithm. But often we do \emph{not} just look for a particular string,

but for string patterns. For example, in program code we need to

identify what are the keywords (\texttt{if}, \texttt{then},

\texttt{while}, \texttt{for}, etc) and what are the identifiers

(variable names). A pattern for identifiers could be stated as: they

start with a letter, followed by zero or more letters, numbers and

underscores. 

%You might also be surprised what

%constraints programming languages impose about numbers: for example

%123 in JSON is OK, but 0123 is not.

%

% The regex for JASON numbers is

%

% -?(0|[1-9][0-9]*)(\.[0-9]+)?([eE][+-]?[0-9]+)?

Often we also face the problem that we are given a string, for example

some user input, and we want to know whether it matches a particular

pattern---is it an email address, for example. In this way we can

exclude user input that would otherwise have nasty effects on our

program (crashing it or making it go into an infinite loop, if not

worse). This kind of ``inspecting'' mechanism is also implemented in

popular network security tools such as Snort and

Bro.\here{www.snort.org}\here{www.bro.org} They scan incoming

network traffic for computer viruses or malicious packets. Similarly

filtering out spam usually involves looking for some signature

(essentially a string pattern). The point is that the fast

Knuth-Morris-Pratt algorithm for strings is not good enough for such

string \emph{patterns}.\smallskip

\defn{Regular expressions} help with conveniently specifying

such patterns. The idea behind regular expressions is that

they are a simple method for describing languages (or sets of

strings)\ldots at least languages we are interested in in

computer science. For example there is no convenient regular

expression for describing the English language short of

enumerating all English words. But they seem useful for

describing for example simple email addresses.\footnote{See

``8 Regular Expressions You Should Know''

\url{http://goo.gl/5LoVX7}} Consider the following regular

expression

\begin{equation}\label{email}

\texttt{[a-z0-9\_.-]+} \;\;\texttt{@}\;\; \texttt{[a-z0-9.-]+} \;\;\texttt{.}\;\; \texttt{[a-z.]\{2,6\}}

\end{equation}

\noindent where the first part, the user name, matches one or more

lowercase letters (\pcode{a-z}), digits (\pcode{0-9}), underscores, dots

and hyphens. The \pcode{+} at the end of the brackets ensures the ``one

or more''. Then comes the email \pcode{@}-sign, followed by the domain

name which must be one or more lowercase letters, digits, underscores,

dots or hyphens (but no underscores).  Finally there must be a dot

followed by the toplevel domain. This toplevel domain must be 2 to 6

lowercase letters including the dot. Example strings which follow this

pattern are:

\begin{lstlisting}[language={},numbers=none,keywordstyle=\color{black}]

niceandsimple@example.org

very.common@example.co.uk

a.little.lengthy.but.fine@dept.example.ac.uk

other.email-with-dash@example.edu

\end{lstlisting}

\noindent

But for example the following two do not

\begin{lstlisting}[language={},numbers=none,keywordstyle=\color{black}]

user@localserver

disposable.style.email.with+symbol@example.com

\end{lstlisting}

\noindent according to the regular expression we specified in line

\eqref{email} above. Whether this is intended or not is a different

question (the second email above is actually an acceptable email

address according to the RFC 5322 standard for email addresses).

As mentioned above, identifiers, or variables, in program code

are often required to satisfy the constraints that they start

with a letter and then can be followed by zero or more letters

or numbers and also can include underscores, but not as the

first character. Such identifiers can be recognised with the

regular expression

\begin{center}

\pcode{[a-zA-Z] [a-zA-Z0-9_]*}

\end{center}

\noindent Possible identifiers that match this regular expression 

are \pcode{x}, \pcode{foo}, \pcode{foo_bar_1}, \pcode{A_very_42_long_object_name},

but not \pcode{_i} and also not \pcode{4you}.

Many programming languages offer libraries that can be used to

validate such strings against regular expressions. Also there

are some common, and I am sure very familiar, ways of how to

construct regular expressions. For example in Scala we have

a library implementing the following regular expressions: 

\begin{center}

\begin{tabular}{lp{9cm}}

\pcode{re*} & matches 0 or more occurrences of preceding 

expression\\

\pcode{re+} & matches 1 or more occurrences of preceding

expression\\

\pcode{re?} &	 matches 0 or 1 occurrence of preceding 

expression\\

\pcode{re\{n\}}	& matches exactly \pcode{n} number of 

occurrences of preceding  expression\\

\pcode{re\{n,m\}} & matches at least \pcode{n} and at most {\tt m}

occurrences of the preceding expression\\

\pcode{[...]} & matches any single character inside the 

brackets\\

\pcode{[^...]} & matches any single character not inside the 

brackets\\

\pcode{...-...} & character ranges\\

\pcode{\\d} & matches digits; equivalent to \pcode{[0-9]}\\

\pcode{.} & matches every character except newline\\

\pcode{(re)}	& groups regular expressions and remembers 

matched text

\end{tabular}

\end{center}

\noindent With this table you can figure out the purpose of the regular

expressions in the web-crawlers shown Figures \ref{crawler1} and

\ref{crawler3}. In Figure~\ref{crawler1}, however, be careful with

the regular expression for http-addresses in Line~\ref{httpline}. It is

intended to be

\[

\pcode{"https?://[^"]*"}

\]

\noindent specifying that http-addresses need to start with a double

quote, then comes \texttt{http} followed by an optional \texttt{s} and

so on\ldots{}until the closing double quote comes at the end of the

address. Normally we would have to escape the double quotes in order to

make sure we interpret the double quote as character, not as double

quote for a string. But Scala's trick with triple quotes allows us to

omit this kind of ugly escaping. As a result we can just write:

\[

\pcode{""""https?://[^"]*"""".r}

\]

\noindent The convention in Scala is that \texttt{.r} converts a string

into a regular expression. I leave it to you to ponder whether this

regular expression really captures all possible web-addresses. If you

need a quick recap about regular expressions and how the match strings,

here is a quick video: \url{https://youtu.be/bgBWp9EIlMM}.

\subsection*{Why Study Regular Expressions?}

Regular expressions were introduced by Kleene in the 1950ies and they

have been object of intense study since then. They are nowadays pretty

much ubiquitous in computer science. There are many libraries

implementing regular expressions. I am sure you have come across them

before (remember the PRA or PEP modules?). 

Why on earth then is there any interest in studying them again in depth

in this module? Well, one answer is in the following two graphs about

regular expression matching in Python, Ruby, JavaScript and Java

(Version 8).

\begin{center}

\begin{tabular}{@{\hspace{-1mm}}c@{\hspace{1.5mm}}c@{}}

\begin{tikzpicture}

\begin{axis}[

    title={Graph: $\texttt{(a*)*\,b}$ and strings 

           $\underbrace{\texttt{a}\ldots \texttt{a}}_{n}$},

    xlabel={$n$},

    x label style={at={(1.05,0.0)}},

    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=5.5cm,

    height=4.5cm, 

    legend entries={Python, Java 8, JavaScript},  

    legend pos=north west,

    legend cell align=left]

\addplot[blue,mark=*, mark options={fill=white}] table {re-python2.data};

\addplot[cyan,mark=*, mark options={fill=white}] table {re-java.data};

\addplot[red,mark=*, mark options={fill=white}] table {re-js.data};

\end{axis}

\end{tikzpicture}

&

\begin{tikzpicture}

\begin{axis}[

    title={Graph: $\texttt{a?\{n\}\,a{\{n\}}}$ and strings 

           $\underbrace{\texttt{a}\ldots \texttt{a}}_{n}$},

    xlabel={$n$},

    x label style={at={(1.05,0.0)}},

    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=5.5cm,

    height=4.5cm, 

    legend entries={Python,Ruby},  

    legend pos=north west,

    legend cell align=left]

\addplot[blue,mark=*, mark options={fill=white}] table {re-python.data};

\addplot[brown,mark=triangle*, mark options={fill=white}] table {re-ruby.data};  

\end{axis}

\end{tikzpicture}

\end{tabular}

\end{center}

\noindent This first graph shows that Python, JavaScript and Java 8 need

approximately 30 seconds to find out that the regular expression

$\texttt{(a*)*\,b}$ does not match strings of 28 \texttt{a}s. Similarly,

the second shows that Python and Ruby need approximately 29 seconds for finding

out whether a string of 28 \texttt{a}s matches the regular expression

\texttt{a?\{28\}\,a\{28\}}.\footnote{In this

example Ruby uses actually the slightly different regular expression

\texttt{a?a?a?...a?a?aaa...aa}, where the \texttt{a?} and \texttt{a}

each occur $n$ times. More such test cases can be found at

\url{https://www.owasp.org/index.php/Regular_expression_Denial_of_Service_-_ReDoS}.}

Admittedly, these regular expressions are carefully chosen to exhibit

this exponential behaviour, but similar ones occur more often than one

wants in ``real life''. For example, on 20 July 2016 a similar regular

expression brought the webpage \href{http://stackexchange.com}{Stack

Exchange} to its knees:

\begin{center}

\url{http://stackstatus.net/post/147710624694/outage-postmortem-july-20-2016}

\end{center}

\noindent I can also highly recommend a cool webtalk from an engineer

from Stack Exchange on the same subject:

\begin{center}

\url{https://vimeo.com/112065252}

\end{center}

\noindent

A similar problem also occurred in the Atom editor:

\begin{center}

\url{http://davidvgalbraith.com/how-i-fixed-atom/}

\end{center}

\noindent

and also when somebody tried to match web-addresses using a 

relatively simple regular expression

\begin{center}

\url{https://www.tutorialdocs.com/article/regex-trap.html}

\end{center}  

\noindent

Finally, on 2 July 2019 Cloudflare had a global outage because of a 

regular expression (they had no outage for the last 6 years).  What

happened is nicely explained in the blog:

\begin{center}

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

\end{center}  

Such troublesome regular expressions are sometimes called \emph{evil

regular expressions} because they have the potential to make regular

expression matching engines to topple over, like in Python, Ruby,

JavaScript and Java 8. This ``toppling over'' is also sometimes called

\emph{catastrophic backtracking}.  I have also seen the term

\emph{eternal matching} used for this.  The problem with evil regular

expressions and catastrophic backtracking is that they can have some

serious consequences, for example, if you use them in your

web-application. The reason is that hackers can look for these instances

where the matching engine behaves badly and then mount a nice DoS-attack

against your application. These attacks are already have their own name:

\emph{Regular Expression Denial of Service Attacks (ReDoS)}.

It will be instructive to look behind the ``scenes'' to find out why

Python and Ruby (and others) behave so badly when matching strings with

evil regular expressions. But we will also look at a relatively simple

algorithm that solves this problem much better than Python and Ruby

do\ldots actually it will be two versions of the algorithm: the first

one will be able in  the example \texttt{a?\{n\}\,a\{n\}} to process strings of

approximately 1,100 \texttt{a}s in 23 seconds, while the second version

will even be able to process up to 11,000(!) in 5 seconds, see the graph

below:

\begin{center}

\begin{tikzpicture}

  \begin{axis}[

    title={Graph: $\texttt{a?\{n\}\,a{\{n\}}}$ and strings 

           $\underbrace{\texttt{a}\ldots \texttt{a}}_{n}$},

    xlabel={$n$},

    x label style={at={(1.05,0.0)}},

    ylabel={time in secs},

    enlargelimits=false,

    xtick={0,3000,...,12000},

    xmax=13000,

    ymax=32,

    ytick={0,5,...,30},

    scaled ticks=false,

    axis lines=left,

    width=7cm,

    height=4.4cm,

    legend entries={Our Algorithm V1, Our Algorithm V2},

    legend pos=outer north east]

\addplot[green,mark=square*,mark options={fill=white}] table {re2.data};

\addplot[black,mark=square*,mark options={fill=white}] table {re3.data};

\end{axis}

\end{tikzpicture}

\end{center}

\noindent And in the case of the regular expression $\texttt{(a*)*\,b}$

and strings of \texttt{a}s we will beat Java 8 by factor of

approximately 1,000,000 (note the scale on the $x$-axis). 

\begin{center}

\begin{tikzpicture}

  \begin{axis}[

    title={Graph: $\texttt{(a*)*\,b}$ and strings 

           $\underbrace{\texttt{a}\ldots \texttt{a}}_{n}$},

    xlabel={$n$},

    x label style={at={(1.05,0.0)}},

    ylabel={time in secs},

    enlargelimits=false,

    ymax=32,

    ytick={0,5,...,30},

    axis lines=left,

    width=7cm,

    height=4.5cm,

    legend entries={Our Algorithm V2},

    legend pos=outer north east]

\addplot[black,mark=square*,mark options={fill=white}] table {re3a.data};

\end{axis}

\end{tikzpicture}

\end{center}

\noindent

You might have wondered above why I looked at the (now) old Java 8: the

reason is that Java 9 and later versions are a bit better, but we will

still beat them hands down with our regex matcher.

\subsection*{Basic Regular Expressions}

The regular expressions shown earlier for Scala, we

will call \emph{extended regular expressions}. The ones we

will mainly study in this module are \emph{basic regular

expressions}, which by convention we will just call

\emph{regular expressions}, if it is clear what we mean. The

attraction of (basic) regular expressions is that many

features of the extended ones are just syntactic sugar.

(Basic) regular expressions are defined by the following

grammar:

\begin{center}

\begin{tabular}{r@{\hspace{1mm}}r@{\hspace{1mm}}l@{\hspace{13mm}}l}

  $r$ & $::=$ &    $\ZERO$          & null language\\

        & $\mid$ & $\ONE$           & empty string / \texttt{""} / []\\

        & $\mid$ & $c$                  & single character\\

        & $\mid$ & $r_1 + r_2$          & alternative / choice\\

        & $\mid$ & $r_1 \cdot r_2$      & sequence\\

        & $\mid$ & $r^*$                & star (zero or more)\\

  \end{tabular}

\end{center}

\noindent Because we overload our notation, there are some

subtleties you should be aware of. When regular expressions

are referred to, then $\ZERO$ (in bold font) does not stand for

the number zero: rather it is a particular pattern that does

not match any string. Similarly, in the context of regular

expressions, $\ONE$ does not stand for the number one but for

a regular expression that matches the empty string. The letter

$c$ stands for any character from the alphabet at hand. Again

in the context of regular expressions, it is a particular

pattern that can match the specified character. You should

also be careful with our overloading of the star: assuming you

have read the handout about our basic mathematical notation,

you will see that in the context of languages (sets of

strings) the star stands for an operation on languages. Here

$r^*$ stands for a regular expression, which is different from

the operation on sets is defined as

\[

A\star\dn \bigcup_{0\le n} A^n

\]

We will use parentheses to disambiguate regular expressions.

Parentheses are not really part of a regular expression, and

indeed we do not need them in our code because there the tree

structure of regular expressions is always clear. But for

writing them down in a more mathematical fashion, parentheses

will be helpful. For example we will write $(r_1 + r_2)^*$,

which is different from, say $r_1 + (r_2)^*$. This can be

seen if we write regular expressions as trees:

\begin{center}