Moved the (outdated) docs to other repositories

docs/explanation.tex

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-\documentclass[envcountsame]{llncs}
-
-\usepackage{amsmath}
-\usepackage[backgroundcolor=white]{todonotes}
-
-\newcommand{\Def}[1]{\emph{#1}}
-\newcommand{\bigO}{\mathcal{O}}
-
-\begin{document}
-\maketitle
-
-\section{Introduction}
-
-Recently automata learning has gained popularity. Learning algorithms are
-applied to real world systems (systems under learning, or SUL for short) and
-this shows some problems.
-
-In the classical active learning algorithms such as $L^\ast$ one supposes a
-teacher to which the algorithm can ask \Def{membership queries} and \Def{
-equivalence queries}. In the former case the algorithms asks the teacher for the
-output (sequence) for a given input sequence. In the latter case the algorithm
-provides the teacher with a hypothesis and the teacher answers with either an
-input sequence on which the hypothesis behaves differently than the SUL or
-answers affirmatively in the case the machines are behaviorally equivalent.
-
-In real world applications we have to implement the teacher ourselves, despite
-the fact that we do not know all the details of the SUL. The membership queries
-are easily implemented by resetting the machine and applying the input. The
-equivalence queries, however, are often impossible to implement. Instead, we
-have to resort to some sort of random testing. Doing random testing naively is
-of course hopeless, as the state space is often too big. Luckily we we have a
-hypothesis at hand, we can use for model based testing.
-
-One standard framework for model based testing is pioneered by Chow and
-Vasilevski. Briefly, the framework supposes prefix sequences which allows us to
-go from the initial state to a given state $s$ (in the model) or even a given
-transition $t \to s$, and suffix sequences which test whether the machine
-actually is in state $s$. If we have the right suffixes and test every
-transition of the model, we can ensure that the SUL is either equivalent or has
-strictly more states. Such a test suite can be constructed with a size
-polynomially in the number of states of the model. This is contrary to
-exhaustive testing or (naive) random testing, where there are exponentially many
-sequences.
-
-For the prefixes we can use any single source shortest path algorithm. In fact,
-if we restrict ourselves to the above framework, this is the best we can do.
-This gives $n$ sequences of length at most $n-1$ (in fact the total sum is at
-most $\frac{1}{2}n(n-1)$).
-
-For the suffixes, we can use the standard Hopcroft algorithm to generate
-seperating sequences. If we want to test a given state $s$, we take the set of
-suffixes (we allow the set of suffixes to depend on the state we want to test)
-to be all seperating sequences for all other states $t$. This set has at most $n
--1$ elements of at most length $n$, again the total sum is $\frac{1}{2}n(n-1)$.
-A natural question arises: can we do better?
-
-In the presence of a distinguishing sequence, Lee and Yannakakis prove that one
-can take a set of suffixes of just $1$ element of at most length $\frac{1}{2}n(n
--1)$. This does not provide an improvement in the worst case scenario. Even
-worse, such a sequence might not exist.
-
-In this paper we propose a testing algorithm which combines the two methods
-described above. The distinguishing sequence might not exist, but the tree
-constructed during the Lee and Yannakakis provides a lot of information which we
-can complement with the classical Hopcroft approach. Despite the fact that this
-is not an improvement in the worst case scenario, this hybrid method enabled us
-to learn an industrial grade machine, which was infeasible to learn with the
-standard methods provided by LearnLib.
-
-
-\section{Preliminaries}
-
-We restrict our attention to \Def{Mealy machines}. Let $I$ (resp. $O$) denote
-the finite set of inputs (resp. outputs). Then a Mealy machine $M$ consists of a
-set of states $S$ with a initial states $s_0$, together with a transition
-function $\delta : I \times S \to S$ and an output function $\lambda : I \times
-S \to O$. Note that we assume machines to be deterministic and total. We also
-assume that our system under learning is a Mealy machine. Both functions $\delta
-$ and $\lambda$ are extended to words in $I^\ast$.
-
-We are in the context of learning, so we will generally denote the hypothesis by
-$H$ and the system under learning by $SUL$. Note that we can assume $H$ to be
-minimal and reachable.
-
-We assume that the alphabets $I$ and $O$ are fixed in these notes.
-\begin{definition}
-	A \Def{set of words $X$ (over $I$)} is a subset $X \subset I^\ast$.
-
-	Given a set of states $S$, then a \Def{family of sets $X$ (over $I$)} is a
-	collection $X = \{X_s\}_{s \in S}$ where each $X_s$ is a set of words.
-\end{definition}
-
-All words we will consider are over $I$, so we will refer to them as simply
-words. The idea of a family of sets was also introduced by Fujiwara. They are
-used to collect sequences which are relevant for a certain state. We define
-some operations on sets and families:
-\newcommand{\tensor}{\otimes}
-\begin{itemize}
-	\item Let $X$ and $Y$ be two sets of words over $I$, then $X \cdot Y$ is the
-	set of all concatenations: $X \cdot Y = \{ x y \,|\, x \in X, y \in Y \}$.
-	\item Let $X^n = X \cdots X$ denote the iterated concatenation and $X^{\leq k}
-	= \bigcup_{n \leq k} X^n$ all concatenations of at most length $k$.
-	In particular $I^n$ are all words of length
-	precisely $n$ and $I^{\leq k}$ are all words of length at most $k$.
-	\item Let $X = \{ X_s \}_{s \in S}$ and $Y = \{ Y_s \}_{s \in S}$ be two
-	families of sets. We define a new family of	words $X \tensor_H Y$ as
-	$(X \tensor_H Y)_s = \{ x y \,|\, x \in X_s, y \in Y_{\delta(s, x)} \}$.
-	Note that this depends on the transitions in the machine $H$.
-	\item Let $X$ be a family and $Y$ just a set of words,
-	then the usual concatenation is defined as $(X \cdot Y)_s = X_s \cdot Y$.
-	\item Let $X$ be a family of sets, then the union $\bigcup X$ forms a set
-	of words.
-\end{itemize}
-
-Let $H$ be a fixed machine and let $\tensor$ denote $\tensor_H$. We define some
-useful sets (which depend on $H$):
-\begin{itemize}
-	\item The set of prefixes $P_s = \{ x \,|\, \text{a shortest } x \text{ such
-	that } \delta(s, x) = t, t \in S \}$. Note that $P_{s_0}$ is particularly
-	interesting. These sets can be constructed by any shortest path algorithm.
-	Note that $P \cdot I$ is a set covering all transitions in $H$.
-	\item The set $W_s = \{ x \,|\, x \text{ seperates } s \text{ and } t, t \in
-	S\}$. This can be constructed using Hopcroft's algorithm or Gill's algorithm
-	if one wants minimal separating sequences.
-	\item If $x$ is an adaptive distinguishing sequence in the sense of Lee and
-	Yannakakis, and let $x_s$ denote the associated UIO for state $x$, we
-	define $Z_s = \{ x_s \}$.
-\end{itemize}
-
-We obtain different methods (note that all test suites are expressed as families
-of sets $X$, the actual test suite is $X_{s_0}$):
-\begin{itemize}
-	\item The originial Chow and Vasilevski (W-method) test suite is given by:
-	$$ P \cdot I^{\leq k+1} \cdot \bigcup W $$
-	which distinguishes $H$ from any non-equivalent machine with at
-	most $|S| + k$ states.
-	\item The Wp-method as described by Fujiwara:
-	$$ (P \cdot I^{\leq k} \cdot \bigcup W) \cup (P \cdot I^{\leq k+1} \tensor W) $$
-	which is a smaller test suite than the W-method, but just as strong. Note
-	that the original description by Fujiwara is more detailed in order to
-	reduce redundancy.
-	\item The method proposed by Lee and Yannakakis:
-	$$ P \cdot I^{\leq k+1} \tensor Z $$
-	which is as big as the Wp-method in the worst case (if it even exists) and
-	just as strong.
-\end{itemize}
-
-An important observation is that the size of $P$, $W$ and $Z$ are polynomially
-in the number of states of $H$, but that the middle part $I^{\leq k+1}$ is
-exponential. If the numbers of states of $SUL$ is known, one can perform a (big)
-exhaustive test. In practice this is not known or has a very large bound. To
-mitigate this we can exhaust $I^{\leq 1}$ and then resort to randomly sample $I
-^\ast$. It is in this sampling phase that we want $W$ and $Z$ to contain the
-least number of elements as every element contributes to the exponential blowup.
-
-Also note that $W$ can also be constructed in different ways. For example taking
-$W_s = \{ u_s \}$, where $u_s$ is a UIO for state $s$ (assuming they all exist)
-gives valid variants of the first two methods. Also if an adaptive
-distinguishing sequence exists, all states have UIOs and we can use the first
-two methods. The third method, however, is slightly smallar as we do not need
-$\bigcup W$ in this case, because the UIOs constructed from an adaptive
-distinguishing sequence share (non-empty) prefixes.
-
-% fix from http://tex.stackexchange.com/questions/103735/list-of-todos-todonotes-is-empty-with-llncs
-\setcounter{tocdepth}{1}
-\listoftodos
-
-\end{document}

docs/test_selection.tex

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-\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 $I^\ast$.