Minor maintenance release

• replace Now by BindNext: #53 and move overloads of Formula.now that don't generate a result immediately to overloads of Formula.next, so the DSL is more clear
• add beEqualAsSetTo RDD matcher
• minor scaladoc fixes
• rename overloads of forAllDStream for more than 1 argument to avoid having to specify the type parameters in all usages

Bug fixes

• Ensure we don't try to cache() generated batches more than once, which throws a Spark exception.
• Fix formatting of print of generated DStreams.
• Bug fix: the state of the formula has to be updated even for empty batches, because the state and output of stateful operators might be updated anyway (e.g. PairDStreamFunctions.reduceByKeyAndWindow)
• Fix base case of safeWordLength #51.

First order quantifiers, many DStreams in properties, and performance improvements

• Add first order quantifiers on letters and optionally use atoms time in now, in the style of TPTL #46.
• Support several DStreams in properties: 1 input 1 derived, 2 input 1 derived, 1 input 2 derived, 2 input 2 derived #16.
• Lazy next form, so the system might scales to complex formulas or with long timeouts #25. Only Next is lazy, which is enough combined with a nested next formula generation.
• Simplify concurrency: synchronization to wait for the Streaming Context to stop; improve usage of parallel collections.
• Fix safeWordLength to return an Option, as it cannot be always statically computed due to first order quantifiers.
• Update to Spark 1.6.2 to get rid of some bugs.

Bug fixing and code cleanup

• Remove dependency to spark-testing-base and multisets in order to fix #36
• Remove unused code from preliminary approaches that were later discarded
• Update Spark to version 1.6.0

Bug fix implementation of temporal properties. Uses of DStreamProp.forAll combining with extending the trait SharedStreamingContextBeforeAfterEach should be replaced by extending the trait DStreamTLProperty and calling forAllDStream. This solves:

• Execution of a test case is now independent from others, as a new streaming context is created for each test case. This is particularly important for stateful DStream transformations
• Replaced uses DynSingleSeqQueueInputDStream by TestInputStream from spark-testing-base, which implements checkpointing correctly
• fixed #32 and #31

First implementation of a temporal logic for testing Spark Streaming with ScalaCheck. It allows to define:

• Generators for DStream defined by temporal logic formulas.
• Properties for testing functions over DStream, using a ScalaCheck generator, and a propositional temporal logic formula as the assertion. DStreamProp.forAll defines a property that is universally quantified over the generated test cases.

Shared Spark context for ScalaCheck generators based on parallelization of lists, through the integration of ScalaCheck and specs2