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"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.

This is the tonic look-and-feel packaged to be distributed with the SQuirreLSQL client. This pluggable look and feel is a free substitute for the default native look and feel of Swing, 'Metal', distributed under the GNU Lesser General Public License. Metal lacks both in usability and aesthetics. It contains considerable graphical noise, distracting the user from the key elements of the GUI. Tonic, on the other hand, tries to provide a clean, balanced look and an improved feel. Tonic is available free of charge both for commercial and non-commercial applications. It lends a professional touch and a very tidy and clean interface to your Swing based applications.

A tool to help visualise API differences between two different versions of a project. Jardiff takes two jar files and outputs all the public API changes as xml, html or plain text. Currently this is command line driven, though we are working on an ant plugin. It requires java 1.4 to run. <h2>Background</h2> At OSjava, we were using a shell script to work out API differences between our release versions. This unfortunately doesn't scale very well to larger projects, the main reason for jardiff was to do the same in a much shorter time (From memory it was taking minutes to get the API diff from two fairly small jars). In it's current state, jardiff can generate the api differences between rt.jar for java 1.4 and java 1.5 in under 20 seconds, so it should scale to even the largest of projects.

The boilerpipe library provides algorithms to detect and remove the surplus "clutter" (boilerplate, templates) around the main textual content of a web page. The library already provides specific strategies for common tasks (for example: news article extraction) and may also be easily extended for individual problem settings. Extracting content is very fast (milliseconds), just needs the input document (no global or site-level information required) and is usually quite accurate. Boilerpipe is a Java library written by Christian Kohlschütter. It is released under the Apache License 2.0. The algorithms used by the library are based on (and extending) some concepts of the paper "Boilerplate Detection using Shallow Text Features" by Christian Kohlschütter et al., presented at WSDM 2010 -- The Third ACM International Conference on Web Search and Data Mining New York City, NY USA.

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.

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.

git-commit-id-plugin is a plugin quite similar to https://fisheye.codehaus.org/browse/mojo/tags/buildnumber-maven-plugin-1.0-beta-4 for example but as buildnumber only supports svn (which is very sad) and cvs (which is even more sad). This plugin makes basic repository information available through maven resources. This can be used to display "what version is this?" or "who has deployed this and when, from which branch?" information at runtime - making it easy to find things like "oh, that isn't deployed yet, I'll test it tomorrow" and making both testers and developers life easier. The data currently exported is like this (that's the end effect from the GitRepositoryState Bean): { "branch" : "testing-maven-git-plugin", "commitTime" : "06.01.1970 @ 16:16:26 CET", "commitId" : "787e39f61f99110e74deed68ab9093088d64b969", "commitUserName" : "Konrad Malawski", "commitUserEmail" : "[email protected]", "commitMessageFull" : "releasing my fun plugin :-) + fixed some typos + cleaned up directory structure + added license etc", "commitMessageShort" : "releasing my fun plugin :-)", "buildTime" : "06.01.1970 @ 16:17:53 CET", "buildUserName" : "Konrad Malawski", "buildUserEmail" : "[email protected]" } Note that the data is exported via maven resource filtering and is really easy to use with spring - which I've explained in detail in this readme https://github.com/ktoso/maven-git-commit-id-plugin

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 project aims to offer an easy-to-extend Java DAS server framework. It offers several advantages: * Implementing data sources is very easy but also flexible and powerful. * Data caching is built into the system, with access to the caching mechanism made available to the data sources. * All aspects of the server are highly configurable, including selecting options where the DAS 1.53 specification offers choices to the implementor. * The latest Java technologies have been used throughout the system to optimise performance and simplify data source development. * Wherever possible the same terminology is used in the API as in the DAS specification and XML - again, making data source development more easy. * The server allows XSLT transforms of the DAS XML to be configured to provide a simple DAS client view (limited to the single DAS source). More details of the DAS protocol, DAS servers and DAS clients can be found at http://www.biodas.org/wiki/Main_Page. The first version of this server is a complete implementation of Distributed Sequence Annotation System (DAS) Version 1.53. If you are interested in learning more about DAS 1.53, the specification is highly recommended as a concise and complete description of the DAS protocol that can be obtained from: http://biodas.org/documents/spec.html