scientificComputing/debugging/lecture/debugging.tex

\chapter{Debugging}

\centerline{\includegraphics[width=0.7\textwidth]{xkcd_debugger}\rotatebox{90}{\footnotesize\url{www.xkcd.com}}}\vspace{4ex}


When writing a program from scratch we almost always make
mistakes. Accordingly, a quite substantial amount of time is invested
into finding and fixing errors. This process is called
\codeterm{debugging}. Don't be frustrated that a self-written program
does not work as intended and produces errors. It is quite exceptional
if a program appears to be working on the first try and, in fact,
should leave you suspicious.

In this chapter we will talk about typical mistakes, how to read and
understand error messages, how to actually debug your program code and
some hints that help to minimize errors.

\section{Types of errors and error messages}

There are a number of different classes of programming errors and it
is good to know the common ones. When we make a programming error
there are some that will lead to corrupted syntax, or invalid
operations and \matlab{} will \codeterm{throw} an error. Throwing an
error ends the execution of a program and there will be an error
messages shown in the command window. With such messages \matlab{}
tries to explain what went wrong and provide a hint on the possible
cause.

Bugs that lead to the termination of the execution may be annoying but
are generally easier to find and fix than logical errors that stay
hidden and the results of, e.g. an analysis, are seemingly correct.

\begin{important}[Try --- catch]
There are ways to \codeterm{catch} errors during \codeterm{runtime}
(i.e. when the program is executed) and handle them in the program.

\begin{lstlisting}[label=trycatch, caption={Try catch clause}]
  try
     y = function_that_throws_an_error(x);
  catch
     y = 0;
  end
\end{lstlisting}

This way of solving errors may seem rather convenient but is
risky. Having a function throwing an error and catching it in the
\codeterm{catch} clause will keep your command line clean but may
obscure logical errors! Take care when using the \codeterm{try-catch
  clause}.
\end{important}


\subsection{\codeterm{Syntax error}}
The most common and easiest to fix type of error. A syntax error
violates the rules (spelling and grammar) of the programming
language. For example every opening parenthesis must be matched by a
closing one or every \keyword{for} loop has to be closed by an
\keyword{end}. Usually, the respective error messages are clear and
the editor will point out and highlight most \codeterm{syntax error}s.

\begin{lstlisting}[label=syntaxerror, caption={Unbalanced parenthesis error.}]
>> mean(random_numbers
                      |
Error: Expression or statement is incorrect--possibly unbalanced (, {, or [.

Did you mean:
>>   mean(random_numbers)
\end{lstlisting}

\subsection{\codeterm{Indexing error}}
Second on the list of common errors are the indexing errors. Usually
\matlab{} gives rather precise infromation about the cause, once you
know what they mean. Consider the following code.

\begin{lstlisting}[label=indexerror, caption={Indexing errors.}]
>> my_array = (1:100);
>> % first try: index 0
>> my_array(0)
Subscript indices must either be real positive integers or logicals.

>> % second try: negative index
>> my_array(-1)
Subscript indices must either be real positive integers or logicals.

>> % third try: a floating point number
>> my_array(5.7)
Subscript indices must either be real positive integers or logicals.

>> % fourth try: a character
>> my_array('z')
Index exceeds matrix dimensions.

>> % fifth try: another character
>> my_array('A')
ans =
     65 % wtf ?!?
\end{lstlisting}

The first two indexing attempts in listing \ref{indexerror} are rather
clear. We are trying to access elements with indices that are
invalid. Remember, indices in \matlab{} start with 1. Negative numbers
and zero are not permitted.  In the third attemp we index using a
floating point number. This fails because indices have to be 'integer'
values.  Using a character as an index (fourth attempt) leads to a
different error message that says that the index exceeds the matrix
dimensions. This indicates that we are trying to read data behind the
length of our variable \codevar{my\_array} which has 100 elements.
One could have expected that the character is an invalid index, but
apparently it is valid but simply too large. The fith attempt finally
succeeds. But why? \matlab{} implicitely converts the \codeterm{char}
to a number and uses this number to address the element in
\varcode{my\_array}. The \codeterm{char} has the ASCII code 65 and
thus the 65th element of \varcode{my_array} is returned.

\subsection{\codeterm{Assignment error}}
This error occurs when we want to write data into a vector.

\subsection{Name error}
\subsection{Arithmetic error}

\section{Logical error}
Sometimes a program runs smoothly and terminates without any
error. This, however, does not necessarily mean that the program is
correct. We may have made a \codeterm{logical error}. Logical errors
are hard to find, \matlab{} has no chance to find this error and can
not help us fixing bugs origination from these. We are on our own but
there are a few strategies that should help us.

\begin{enumerate}
\item Be sceptical: especially when a program executes without any
  complaint on the first try.
\item Clean code: Structure your code that you can easily read
  it. Comment, but only where necessary. Correctly indent your
  code. Use descriptive variable and function names.
\item Keep it simple (below).
\item Read error messages, try to understand what \matlab{} wants to
  tell.
\item Use scripts and functions and call them from the command
  line. \matlab{} can then provide you with more information. It will
  then point to the line where the error happens.
\item If you still find yourself in trouble: Apply debugging
  strategies to find and fix bugs (below).
\end{enumerate}


\subsection{Avoiding errors}
It would be great if we could just sit down write a program, run it
and be done. Most likely this will not happen. Rather, we will make
mistakes and have to bebug the code. There are a few guidelines that
help to reduce the number of errors.

\subsection{The Kiss  principle: 'Keep it small and simple' or 'simple and stupid'}

\shortquote{Debugging time increases as a square of the program's
  size.}{Chris Wenham}

Break down your programming problems into small parts (functions) that
do exactly one thing. This has already been discussed in the context
of writing scripts and functions. In parts this is just a matter of
feeling overwhelmed by 1000 lines of code. Further, with each task
that you incorporate into the same script the probability of naming
conflicts (same or similar names for variables) increases. Remembering
the meaning of a certain variable that was defined in the beginning of
the script is just hard.


\shortquote{Everyone knows that debugging is twice as hard as writing
  a program in the first place. So if you're as clever as you can be
  when you write it, how will you ever debug it?}{Brian Kernighan}

Many tasks within an analysis can be squashed into a single line of
code. This saves some space in the file, reduces the effort of coming
up with variable names and simply looks so much more competent than a
collection of very simple lines. Consider the following listing
(listing~\ref{easyvscomplicated}). Both parts of the listing solve the
same problem but the second one breaks the task down to a sequence of
easy-to-understand commands. Finding logical and also syntactic errors
is much easier in the second case. The first version is perfectly fine
but it requires a deep understanding of the applied functions and also
the task at hand.

\begin{lstlisting}[label=easyvscomplicated, caption={Converting a series of spike times into the firing rate as a function of time. Many tasks can be solved with a single line of code. But is this readable?}]
% the one-liner
rate = conv(full(sparse(1, round(spike_times/dt), 1, 1, length(time))), kernel, 'same');

% easier to read
rate = zeros(size(time));
spike_indices = round(spike_times/dt);
rate(spike_indices) = 1;
rate = conv(rate, kernel, 'same');
\end{lstlisting}

The preferred way depends on several considerations. (i) How deep is
your personal understanding of the programming language?  (ii) What
about the programming skills of your target audience or other people
that may depend on your code? (iii) Is one solution faster or uses
less resources than the other? (iv) How much do you have to invest
into the development of the most elegant solution relative to its
importance in the project? The decision is up to you.

\subsection{Read error messages carefully and call programs from the command line.}



\section{Error messages}


\begin{ibox}[tp]{\label{stacktracebox}Stacktrace or Stack Traceback}

  
\end{ibox}


Es hilft ungemein, wenn zusammengeh\"orige Skripte und Funktionen im
gleichen Ordner auf der Festplatte zu finden sind. Es bietet sich also
an, f\"ur jede Analyse einen eigenen Ordner anzulegen und in diesem
die zugeh\"origen \codeterm{m-files} abzulegen. Auf eine tiefere
Schachtelung in weitere Unterordner kann in der Regel verzichtet
werden. \matlab{} erzeugt einen ``MATLAB'' Ordner im eigenen
\file{Documents} (Linux) oder \file{Eigene Dokumente} (Windows)
Ordner. Es bietet sich an, diesen Ordner als Wurzelverzeichnis f\"ur
eigene Arbeiten zu verwenden. Nat\"urlich kann auch jeder andere Ort
gew\"ahlt werden. In dem Beispiel in \figref{fileorganizationfig} wird
innerhalb dieses Ordners f\"ur jedes Projekt ein eigener Unterordner
erstellt, in welchem wiederum f\"ur jedes Problem, jede Analyse ein
weiterer Unterodner erstellt wird. In diesen liegen sowohl die
ben\"otigten \codeterm{m-files} also auch die Resultate der Analyse
(Abbildungen, Daten-Dateien). Zu bemerken sind noch zwei weitere
Dinge. Im Projektordner existiert ein Skript (analysis.m), das dazu
gedacht ist, alle Analysen aufzurufen. Des Weiteren gibt es parallel
zu den Projektordnern einen \file{functions}-Ordner in dem Funktionen
liegen, die in mehr als einem Projekt oder einer Analyse gebraucht
werden.

\begin{figure}[tp]
  \includegraphics[width=0.75\textwidth]{no_bug}
  \titlecaption{\label{fileorganizationfig} M\"ogliche Organisation von
    Programmcode im Dateisystem.}{ F\"ur jedes Projekt werden
    Unterordner f\"ur die einzelnen Analysen angelegt. Auf Ebene des
    Projektes k\"onnte es ein Skript (hier ``analysis.m'') geben,
    welches alle Analysen in den Unterordnern anst\"o{\ss}t.}
\end{figure}


\Section{Namensgebung von Funktionen und Skripten}

\matlab{} sucht Funktionen und Skripte ausschlie{\ss}lich anhand des
Namens. Dabei spielt die Gro{\ss}- und Kleinschreibung eine Rolle. Die
beiden Dateien \file{test\_funktion.m} und \file{Test\_Funktion.m}
zwei unterschiedliche Funktionen benennen k\"onnen. Diese Art der
Variation des Namens ist nat\"urlich nicht sinnvoll. Sie tr\"agt keine
Information \"uber den Unterschied der beiden Funktionen. Auch sagt
der Name nahezu nichts \"uber den Zweck der Funktion aus.

Die Namensgebung f\"allt mitunter nicht leicht --- manchmal ist es
sogar der schwierigste Aspekt des Programmierens!  Ausdrucksstarke
Namen zu finden lohnt sich aber. Ausdrucksstark bedeutet, dass sich
aus dem Namen R\"uckschl\"usse auf den Zweck ziehen lassen sollte.

\begin{important}[Benennung von Funktionen und Skripten]
  Die Namen von Funktionen und Skripten sollten m\"oglichst viel \"uber
  die Funktionsweise oder den Zweck aussagen (\file{firingrates.m}
  statt \file{uebung.m}). Gute Namen f\"ur Funktionen und Skripte sind
  die beste Dokumentation.
\end{important}

In Namen verbietet \matlab{} verbietet Leerzeichen, Sonderzeichen und
Umlaute. Namen d\"urfen auch nicht mit Zahlen anfangen. Es mach f\"ur
die Namensgebung selbst keine weiteren Vorgaben. Allerdings folgt die
Benennung der in \matlab{} vordefinierten Funktionen gewissen Mustern:
\begin{itemize}
\item Namen werden immer klein geschrieben.
\item Es werden gerne Abk\"urzungen eingesetzt (z.B. \code{xcorr()}
  f\"ur die Kreuzkorrelation oder \code{repmat()} f\"ur ``repeat matrix'')
\item Funktionen, die zwischen Formaten konvertieren sind immer nach
  dem Muster ``format2format'' (z.B. \code{num2str()} f\"ur die
  Konvertierung ``number to string'', Umwandlung eines numerischen
  Wertes in einen Text) benannt.
\end{itemize}


\begin{important}[Benennung von Variablen]
  Die Namen von Variablen sollten m\"oglichst viel \"uber ihren Inhalt
  aussagen (\varcode{spike\_count} statt \varcode{x}). Gute Namen
  f\"ur Variablen sind die beste Dokumentation.
\end{important}

 
\begin{lstlisting}[label=chaoticcode, caption={Un\"ubersichtliche Implementation des Random-walk.}]

\end{lstlisting}

\pagebreak[4]

\begin{lstlisting}[label=cleancode, caption={\"Ubersichtliche Implementation des Random-walk.}]
num_runs = 10;
max_steps = 1000;
positions = zeros(max_steps, num_runs);

for run = 1:num_runs
    for step = 2:max_steps
        x = randn(1);
        if x < 0
            positions(step, run) = positions(step-1, run) + 1;
        elseif x > 0
            positions(step, run) = positions(step-1, run) - 1;
        end
    end
end
\end{lstlisting}


% \begin{exercise}{logicalVector.m}{logicalVector.out}
%   Erstelle einen Vektor \varcode{x} mit den Werten 0--10.
%   \begin{enumerate}
%   \item F\"uhre aus: \varcode{y = x < 5}
%   \item Gib den Inhalt von \varcode{y} auf dem Bildschirm aus.
%   \item  Was ist der Datentyp von \varcode{y}?
%   \item  Gibt alle Elemente aus \varcode{x} zur\"uck, die kleiner als 5 sind.
%   \end{enumerate}
%   \pagebreak[4]
% \end{exercise}