\subsubsection{Augmented DS-method}
\label{sec:randomPrefix}

In order to reduce the number of tests, Chow~\cite{Ch78} and
Vasilevskii~\cite{vasilevskii1973failure} pioneered the so called W-method. In
their framework a test query consists of a prefix $p$ bringing the SUL to a
specific state, a (random) middle part $m$ and a suffix $s$ assuring that the
SUL is in the appropriate state. This results in a test suite of the form $P
I^{\leq k} W$, where $P$ is a set of (shortest) access sequences, $I^{\leq k}$
the set of all sequences of length at most $k$, and $W$ is a characterization
set. Classically, this characterization set is constructed by taking the set of
all (pairwise) separating sequences. For $k=1$ this test suite is complete in
the sense that if the SUL passes all tests, then either the SUL is equivalent to
the specification or the SUL has strictly more states than the specification. By
increasing $k$ we can check additional states.

We tried using the W-method as implemented by LearnLib to find counterexamples.
The generated test suite, however, was still too big in our learning context.
Fujiwara et al \cite{FBKAG91} observed that it is possible to let the set $W$ depend on the state the
SUL is supposed to be. This allows us to only take a subset of $W$ which
is relevant for a specific state. This slightly reduces the test suite which is
as powerful as the full test suite. This methods is known as the Wp-method. More
importantly, this observation allows for generalizations where we can carefully
pick the suffixes.

In the presence of an (adaptive) distinguishing sequence one can take $W$ to be a
single suffix, greatly reducing the test suite. Lee and Yannakakis \cite{LYa94} describe an
algorithm (which we will refer to as the LY algorithm) to efficiently
construct this sequence, if it exists. In our case, unfortunately, most
hypotheses did not enjoy existence of an adaptive distinguishing sequence. In
these cases the incomplete result from the LY algorithm still contained a lot of
information which we augmented by pairwise separating sequences.

\begin{figure}
  \centering \includegraphics[width=\textwidth]{hyp_20_partial_ds.pdf}
  \caption{A small part of an incomplete distinguishing sequence as produced by
  the LY algorithm. Leaves contain a set of possible initial states, inner nodes
  have input sequences and edges correspond to different output symbols (of
  which we only drew some), where Q stands for quiescence.}
  \label{fig:distinguishing-sequence}
\end{figure}

As an example we show an incomplete adaptive distinguishing sequence for one of
the hypothesis in Figure~\ref{fig:distinguishing-sequence}. When we apply the
input sequence I46 I6.0 I10 I19 I31.0 I37.3 I9.2 and observe outputs O9 O3.3 Q ...
O28.0, we know for sure that the SUL was in state 788. Unfortunately not all
path lead to a singleton set. When for instance we apply the sequence I46 I6.0
I10 and observe the outputs O9 O3.14 Q, we know for sure that the SUL was in one
of the states 18, 133, 1287 or 1295. In these cases we have to perform more
experiments and we resort to pairwise separating sequences.

We note that this augmented DS-method is in the worst case not any better than
the classical Wp-method. In our case, however, it greatly reduced the test
suites.

Once we have our set of suffixes, which we call $Z$ now, our test algorithm
works as follows. The algorithm first exhausts the set $P I^{\leq 1} Z$. If this
does not provide a counterexample, we will randomly pick test queries from $P
I^2 I^\ast Z$, where the algorithm samples uniformly from $P$, $I^2$ and $Z$ (if
$Z$ contains more that $1$ sequence for the supposed state) and with a geometric
distribution on $I^\ast$.