update doc

README.md

@@ -67,9 +67,7 @@ For developing the code base of LearnLib it is suggested to use one of the major
 
 * For [IntelliJ IDEA][intellij]:
   1. Select `File` -> `New` -> `Project from existing sources` and select the folder containing the development checkout.
-  1. Choose "Import Project from external model", select "Maven" and click `Next`.
-  1. Configure the project to your liking but make sure to check "Import Maven projects automatically" and have "Generated sources folders" set to "Detect automatically".
-  1. Click `Next` until the project is imported (no Maven profile needs to be selected).
+  1. Choose "Import Project from external model", select "Maven" and click `Create`.
   1. In order to have both development versions of AutomataLib and LearnLib available at once, continue to import AutomataLib as documented in the project's README, but choose `File` -> `New` -> `Module from existing sources` as the first step.
 
 * For [Eclipse][eclipse]:

oracles/equivalence-oracles/src/main/java/de/learnlib/oracle/equivalence/RandomWMethodEQOracle.java

@@ -43,14 +43,15 @@
 import net.automatalib.word.WordBuilder;
 
 /**
- * Implements an equivalence test by applying the W-method test on the given hypothesis automaton, as described in
- * <a href="https://doi.org/10.1109/TSE.1978.231496">Testing Software Design Modeled by Finite-State Machines</a> by
- * T.&nbsp;S.&nbsp;Chow. Instead of enumerating the test suite in order, this is a sampling implementation:
- * <ul>
- * <li>1. sample uniformly from the transitions for a prefix</li>
- * <li>2. sample geometrically a random word</li>
- * <li>3. sample a word from the set of suffixes / state identifiers</li>
- * </ul>
+ * Implements an equivalence test based on a randomized version of the W-method as described in <a
+ * href="https://arxiv.org/abs/1611.02429">Complementing Model Learning with Mutation-Based Fuzzing</a> by Rick
+ * Smetsers, Joshua Moerman, Mark Janssen, Sicco Verwer. Instead of enumerating the test suite in order, this is a
+ * sampling implementation:
+ * <ol>
+ * <li>sample uniformly from the transitions for a prefix</li>
+ * <li>sample geometrically a random word</li>
+ * <li>sample a word from the set of suffixes / state identifiers</li>
+ * </ol>
  * There are two parameters: minimalSize determines the minimal size of the random word, this is useful when one first
  * performs a W(p)-method with some depth and continue with this randomized tester from that depth onward. The second
  * parameter rndLength determines the expected length of the random word. (The expected length in effect is minimalSize

oracles/equivalence-oracles/src/main/java/de/learnlib/oracle/equivalence/RandomWpMethodEQOracle.java

@@ -44,15 +44,16 @@
 import net.automatalib.word.WordBuilder;
 
 /**
- * Implements an equivalence test by applying the Wp-method test on the given hypothesis automaton, as described in <a
- * href="https://doi.org/10.1109/32.87284">Test selection based on finite state models</a> by S.&nbsp;Fujiwara et al.
- * Instead of enumerating the test suite in order, this is a sampling implementation:
- * <ul>
- * <li>1. sample uniformly from the states for a prefix</li>
- * <li>2. sample geometrically a random word</li>
- * <li>3. sample a word from the set of suffixes / state identifiers</li>
- * </ul>
- * There are two parameters:minimalSize determines the minimal size of the random word, this is useful when one first
+ * Implements an equivalence test based on a randomized version of the W(p)-method as described in <a
+ * href="https://arxiv.org/abs/1611.02429">Complementing Model Learning with Mutation-Based Fuzzing</a> by Rick
+ * Smetsers, Joshua Moerman, Mark Janssen, Sicco Verwer. Instead of enumerating the test suite in order, this is a
+ * sampling implementation:
+ * <ol>
+ * <li>sample uniformly from the states for a prefix</li>
+ * <li>sample geometrically a random word</li>
+ * <li>sample a word from the set of suffixes / state identifiers</li>
+ * </ol>
+ * There are two parameters: minimalSize determines the minimal size of the random word, this is useful when one first
  * performs a W(p)-method with some depth and continue with this randomized tester from that depth onward. The second
  * parameter rndLength determines the expected length of the random word. (The expected length in effect is minimalSize
  * + rndLength.) In the unbounded case it will not terminate for a correct hypothesis.