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Randoop is an automatic unit test generator for Java. It automatically creates unit tests for your classes, in JUnit format. Randoop generates unit tests using feedback-directed random test generation. In a nutshell, this technique randomly, but smartly, generates sequences of methods and constructor invocations for the classes under test, and uses the sequences to create tests. Randoop executes the sequences it creates, using the results of the execution to create assertions that capture the behavior or your program and that catch bugs. Randoop has created tests that find previously unkwon errors even in widely-used libraries including Sun and IBM's JDKs. A .NET version of Randoop, used internally at Microsoft, has been used successfully by a team of test engineers to find errors in a core .NET component that has been heavily tested for years. Randoop's combination of randomized test generation and test execution results in a highly effective test generation technique.

Module that defines the HttpChannel API. HttpChannels abstract complex download and upload steps into a simple and easy to use NIO Channel. NIO Channels can be wrapped into an InputStream or OutputStream and used in any way you may find possible to. Aside from that, Channels can be used natively in most next-gen libraries, meaning that you don't even need to wrap anything, just start writing or reading data to or from the channel wth a ByteBuffer. Anyone using the library should try to rely on code from this module only and, only if necessary, on configuration classes that are implementation specific. Relying on any other resource or class is considered an error and should NOT be done. One of the most interesting usages of channels for uploads and download is that you can easily copy data straight from one channel to the other, with less than 10 lines of code! Also, channels allows the implementation of a "tee" mechanism, in which data redden from a single channel can be copied to several other channels on the fly!

Jadex Rules is a small lightweight rule engine, which currently employs the well-known Rete algorithm for highly efficient rule matching. Jadex rules is therefore similar to other rule engines like JESS and Drools. Despite the similarities there are also important differences between these systems: * Jadex Rules is very small and intended to be used as component of other software. Even though rules can be specified in a Java dialect as well as (a small variation of) the CLIPS language its primary usage is on the API level. Jadex Rules is currently the core component of the Jadex BDI reasoning engine. * Jadex Rules cleanly separates between state and rule representation. This allows the state implementation as well as the matcher to be flexibly exchanged. Some experiments have e.g. been conducted with a Jena representation. Regarding the matcher, it is planned to support also the Treat algorithm, which has a lower memory footprint than Rete. * Jadex Rules pays close attention to rule debugging. The state as well as the rete engine can be observed at runtime. The rule debugger provides functionalities to execute a rule program stepwise and also use rule breakpoints to stop the execution at those points.

This is the brand new, re-written from scratch plugin for ANTLR v3. Previous valiant efforts all suffered from being unable to modify the ANTLR Tool itself to provide support not just for Maven oriented things but any other tool that might wish to invoke ANTLR without resorting to the command line interface. Rather than try to shoe-horn new code into the existing Mojo (in fact I think that by incorporating a patch supplied by someone I ended up with tow versions of the Mojo, I elected to rewrite everything from scratch, including the documentation, so that we might end up with a perfect Mojo that can do everything that ANTLR v3 supports such as imported grammar processing, proper support for library directories and locating token files from generated sources, and so on. In the end I decided to also change the the ANTLR Tool.java code so that it would be the provider of all the things that a build tool needs, rather than delegating things to 5 different tools. So, things like dependencies, dependency sorting, option tracking, generating sources and so on are all folded back in to ANTLR's Tool.java code, where they belong, and they now provide a public interface to anyone that might want to interface with them. One other goal of this rewrite was to completely document the whole thing to death. Hence even this pom has more comments than funcitonal elements, in case I get run over by a bus or fall off a cliff while skiing. Jim Idle - March 2009

Jadex Rules is a small lightweight rule engine, which currently employs the well-known Rete algorithm for highly efficient rule matching. Jadex rules is therefore similar to other rule engines like JESS and Drools. Despite the similarities there are also important differences between these systems: * Jadex Rules is very small and intended to be used as component of other software. Even though rules can be specified in a Java dialect as well as (a small variation of) the CLIPS language its primary usage is on the API level. Jadex Rules is currently the core component of the Jadex BDI reasoning engine. * Jadex Rules cleanly separates between state and rule representation. This allows the state implementation as well as the matcher to be flexibly exchanged. Some experiments have e.g. been conducted with a Jena representation. Regarding the matcher, it is planned to support also the Treat algorithm, which has a lower memory footprint than Rete. * Jadex Rules pays close attention to rule debugging. The state as well as the rete engine can be observed at runtime. The rule debugger provides functionalities to execute a rule program stepwise and also use rule breakpoints to stop the execution at those points.

OpenIMAJ (Open Intelligent Multimedia in Java) is a collection of libraries and tools for multimedia analysis written in the Java programming language. OpenIMAJ intends to be the first truly complete multimedia analysis library and contains modules for analysing images, videos, text, audio and even webpages. The OpenIMAJ image and video analysis and feature extraction modules contain methods for processing visual content and extracting state-of-the-art features, including SIFT. The OpenIMAJ clustering and nearest-neighbour libraries contain efficient, multi-threaded implementations of clustering algorithms including Hierarchical K-Means and Approximate K-Means. The clustering library makes it possible to easily create visual-bag-of-words representations for images and video with very large vocabularies. The text-analysis modules contain implementations of a statistical language classifier and low-level processing pipeline. A number of modules deal with content creation, including interactive slideshows and animations. The hardware integration modules allow cross-platform integration with devices including webcams, the Microsoft Kinect, and even devices such as GPS's. OpenIMAJ also incorporates a number of tools to enable extremely-large-scale multimedia analysis using a distributed computing approach based on Apache Hadoop.