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This open source software is a reference implementation, consisting of software and related files, for the OMG specification called the Semantics of a Foundational Subset for Executable UML Models (fUML). The reference implementation is intended to implement the execution semantics of UML activity models, accepting an XMI file from a conformant UML model as its input and providing an execution trace of the selected activity model(s) as its output. The core execution engine, which is directly generated from the normative syntactic and semantic models for fUML, may also be used as a library implementation of fUML in other software.

Rule Learner is an open source Java library for learning business rules from historical data. It supports supervised Machine Learning by incorporating well-known algorithms such as RIPPER and C4.5 provided by open source tools such as Weka. Rule Learner is implemented as a Machine Learning component of OpenRules Decision Manager. It allows business analysts to place their historical data in simple Excel tables and to automatically discover business rules that find certain pattern in this data. The generated rules are human readable and presented in Excel format executable by OpenRules Decision Manager.

The RestFixture is a FitNesse (http://fitnesse.org) fixture that allows developers and/or product owners to write test fixtures for REST services with simplicity in mind. The idea is to write tests that are self documenting and easy to write and read, without the need to write Java code. The fixture allows test writers to express tests as actions (any of the allowed HTTP methods) to operate on resource URIs and express expectations on the content of the return code, headers and body. All without writing one single line of Java code. And it also works as a living/executable documentation of the API.

"Tests with Parameters" allows you to simply add parameters to your JUnit test methods. TwiP calls such methods with all possible combinations of their parameters... or at least some reasonable subset of commonly failing values in the case of Integers, etc. You can further reduce these values with an assume expression in an annotation, e.g. ">= 0". Alternatively you can specify a static method or field to provide the values for your test method(s), if you want to test with other than the default values. By using TwiP you change the semantics of your tests from existence to for-all quantifiers, i.e. you specify "all ravens are black" instead of "Abraxas is black", "Toni is black", etc. This moves your tests closer to an executable specification, so TwiP is a very nice addition to BDD.