alasdairforsythe/text-english-code-fiction-nonfiction

text stringlengths 0 634k
from the logs and replaced with standardized text. We refer to the process of
enforcing these requirements and delineating the log into events as
the \emph{abstraction} step. This enables SBLD to treat events
like ``2019-04-05 19:19:22.441 CEST: Alice calls Bob'' and ``2019-04-07
13:12:11.337 CEST: Alice calls Bob'' as two instances of the same
generic event "Alice calls Bob". The appropriate degree of abstraction
and how to meaningfully delineate a log will be context-dependent
and thus we require the user to perform these steps before using SBLD.
In the current paper we use an abstraction mechanism
and dataset generously provided by \CiscoNorway{our industrial partner}.

\renewcommand{\Ncf}{\ensuremath{\text{N}_\text{FI}}} %
\renewcommand{\Nuf}{\ensuremath{\text{N}_\text{FE}}} %
\renewcommand{\Ncs}{\ensuremath{\text{N}_\text{PI}}} %
\renewcommand{\Nus}{\ensuremath{\text{N}_\text{PE}}} %

\head{Computing coverage and event relevance} SBLD requires an assumption about what makes an event \emph{relevant}
and a method for computing this relevance. Our method takes inspiration
from Spectrum-Based Fault Localization (SBFL) in which the suspiciousness
or fault-proneness of a program statement is treated as a function of
the number of times the statement was activated in a failing test case,
combined with the number of times it is skipped in a passing test case~\cite{Jones2002,Abreu2007,Abreu2009}.
The four primitives that need to be computed are shown on the right-hand side in Table~\ref{table:measures}.
We treat each abstracted event as a statement and study their occurrences
in the logs like Fault Localization tracks the activation of statements in test cases.
We compute the analysis primitives by devising a binary
\emph{coverage matrix} whose columns represent every unique event
observed in the set of failing and successful logs while each row $r$
represents a log and tracks whether the event at column $c$ occurred in
log $r$ (1), or not (0), as shown in Figure~\ref{fig:approach}.

By computing these primitives, we can rank each event by using an
\emph{interestingness measure} (also referred to as ranking
metric, heuristic, or similarity coefficient~\cite{Wong2016}).
The choice of interestingness measure
is ultimately left to the user, as these are context dependent and
there is no generally optimal choice of interestingness measure~\cite{Yoo2014}.
In this paper we consider a
selection of nine interestingness measures prominent in the literature
and a simple metric that emphasizes the events that exclusively occur
in failing logs in the spirit of the \emph{union model} discussed
by Renieres et al.~\cite{renieres2003:fault}. We
report on the median performance of these interestingness measures with the intention of providing a
representative, yet unbiased, result. The ten measures considered are
precisely defined in Table~\ref{table:measures}.

\begin{table*}
\centering
\begin{tabular}{c@{\hspace{10mm}}c}
{\renewcommand{\arraystretch}{1.7} %
\begin{tabular}{lc}
\toprule
measure & formula \\\midrule

Tarantula \cite{Jones2001,Jones2002} & %

\( \frac{ \frac{ \cef{} }{ \cef{} + \cnf{} } }{ \frac{ \cef{} }{ \cef{} + \cnf{} } + \frac{ \cep{} }{ \cep{} + \cnp{} } } \)

\\

Jaccard \cite{Jaccard1912,Chen2002} & %

\( \frac{ \Ncf }{ \Ncf + \Nuf + \Ncs } \)

\\

Ochiai \cite{Ochiai1957,Abreu2006} & %

\( \frac{ \Ncf }{ \sqrt{ ( \cef + \cnf ) \times ( \cef + \cep ) } } \)

\\

Ochiai2 \cite{Ochiai1957, Naish2011} & %

\( \frac{ \Aef \times \Anp }{ \sqrt{ ( \Aef + \Aep ) \times ( \Anf + \Anp ) \times ( \Aef + \Anf) \times ( \Aep + \Anp ) } } \)

\\

Zoltar \cite{Gonzalez2007} & %

\( \frac{ \Ncf }{ \Ncf + \Nuf + \Ncs + \frac { 10000 \times \Nuf \times \Ncs }{ \Ncf } } \)

\\

D$^\star$ \cite{Wong2014} (we use $\star = 2$) & %

\( \frac{ (\cef)^\star }{ \cnf + \cep } \)

\\

O$^p$ \cite{Naish2011} & %

\( \Aef - \frac{ \Aep }{ \Aep + \Anp + 1} \)

\\

Wong3 \cite{Wong2007,Wong2010} &

\( \Aef - h, \text{where~} h = \left\{
\scalebox{.8}{\(\renewcommand{\arraystretch}{1} %

from the logs and replaced with standardized text. We refer to the process of

enforcing these requirements and delineating the log into events as

the \emph{abstraction} step. This enables SBLD to treat events

like ``2019-04-05 19:19:22.441 CEST: Alice calls Bob'' and ``2019-04-07

13:12:11.337 CEST: Alice calls Bob'' as two instances of the same

generic event "Alice calls Bob". The appropriate degree of abstraction

and how to meaningfully delineate a log will be context-dependent

and thus we require the user to perform these steps before using SBLD.

In the current paper we use an abstraction mechanism

and dataset generously provided by \CiscoNorway{our industrial partner}.

\renewcommand{\Ncf}{\ensuremath{\text{N}_\text{FI}}} %

\renewcommand{\Nuf}{\ensuremath{\text{N}_\text{FE}}} %

\renewcommand{\Ncs}{\ensuremath{\text{N}_\text{PI}}} %

\renewcommand{\Nus}{\ensuremath{\text{N}_\text{PE}}} %

\head{Computing coverage and event relevance} SBLD requires an assumption about what makes an event \emph{relevant}

and a method for computing this relevance. Our method takes inspiration

from Spectrum-Based Fault Localization (SBFL) in which the suspiciousness

or fault-proneness of a program statement is treated as a function of

the number of times the statement was activated in a failing test case,

combined with the number of times it is skipped in a passing test case~\cite{Jones2002,Abreu2007,Abreu2009}.

The four primitives that need to be computed are shown on the right-hand side in Table~\ref{table:measures}.

We treat each abstracted event as a statement and study their occurrences

in the logs like Fault Localization tracks the activation of statements in test cases.

We compute the analysis primitives by devising a binary

\emph{coverage matrix} whose columns represent every unique event

observed in the set of failing and successful logs while each row $r$

represents a log and tracks whether the event at column $c$ occurred in

log $r$ (1), or not (0), as shown in Figure~\ref{fig:approach}.

By computing these primitives, we can rank each event by using an

\emph{interestingness measure} (also referred to as ranking

metric, heuristic, or similarity coefficient~\cite{Wong2016}).

The choice of interestingness measure

is ultimately left to the user, as these are context dependent and

there is no generally optimal choice of interestingness measure~\cite{Yoo2014}.

In this paper we consider a

selection of nine interestingness measures prominent in the literature

and a simple metric that emphasizes the events that exclusively occur

in failing logs in the spirit of the \emph{union model} discussed

by Renieres et al.~\cite{renieres2003:fault}. We

report on the median performance of these interestingness measures with the intention of providing a

representative, yet unbiased, result. The ten measures considered are

precisely defined in Table~\ref{table:measures}.

\begin{table*}

\centering

\begin{tabular}{c@{\hspace{10mm}}c}

{\renewcommand{\arraystretch}{1.7} %

\begin{tabular}{lc}

\toprule

measure & formula \\\midrule

Tarantula \cite{Jones2001,Jones2002} & %

$ \frac{ \frac{ \cef{} }{ \cef{} + \cnf{} } }{ \frac{ \cef{} }{ \cef{} + \cnf{} } + \frac{ \cep{} }{ \cep{} + \cnp{} } } $

\\

Jaccard \cite{Jaccard1912,Chen2002} & %

$ \frac{ \Ncf }{ \Ncf + \Nuf + \Ncs } $

\\

Ochiai \cite{Ochiai1957,Abreu2006} & %

$ \frac{ \Ncf }{ \sqrt{ ( \cef + \cnf ) \times ( \cef + \cep ) } } $

\\

Ochiai2 \cite{Ochiai1957, Naish2011} & %

$ \frac{ \Aef \times \Anp }{ \sqrt{ ( \Aef + \Aep ) \times ( \Anf + \Anp ) \times ( \Aef + \Anf) \times ( \Aep + \Anp ) } } $

\\

Zoltar \cite{Gonzalez2007} & %

$ \frac{ \Ncf }{ \Ncf + \Nuf + \Ncs + \frac { 10000 \times \Nuf \times \Ncs }{ \Ncf } } $

\\

D$^\star$ \cite{Wong2014} (we use $\star = 2$) & %

$ \frac{ (\cef)^\star }{ \cnf + \cep } $

\\

O$^p$ \cite{Naish2011} & %

$ \Aef - \frac{ \Aep }{ \Aep + \Anp + 1} $

\\

Wong3 \cite{Wong2007,Wong2010} &

\( \Aef - h, \text{where~} h = \left\{

\scalebox{.8}{\(\renewcommand{\arraystretch}{1} %