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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.

<p>PMD scans Java source code and looks for potential problems like:</p> <ul> <li>Possible bugs - empty try/catch/finally/switch statements</li> <li>Dead code - unused local variables, parameters and private methods</li> <li>Suboptimal code - wasteful String/StringBuffer usage</li> <li>Overcomplicated expressions - unnecessary if statements, for loops that could be while loops</li> <li>Duplicate code - copied/pasted code means copied/pasted bugs</li> </ul> <p>You can <b><a href="http://sourceforge.net/project/showfiles.php?group_id=56262">download everything from here</a></b>, and you can get an overview of all the rules at the <a href="rules/index.html">rulesets index</a> page.</p> <p>PMD is <a href="integrations.html">integrated</a> with JDeveloper, Eclipse, JEdit, JBuilder, BlueJ, CodeGuide, NetBeans/Sun Java Studio Enterprise/Creator, IntelliJ IDEA, TextPad, Maven, Ant, Gel, JCreator, and Emacs.</p>

<p>PMD scans Java source code and looks for potential problems like:</p> <ul> <li>Possible bugs - empty try/catch/finally/switch statements</li> <li>Dead code - unused local variables, parameters and private methods</li> <li>Suboptimal code - wasteful String/StringBuffer usage</li> <li>Overcomplicated expressions - unnecessary if statements, for loops that could be while loops</li> <li>Duplicate code - copied/pasted code means copied/pasted bugs</li> </ul> <p>You can <b><a href="http://sourceforge.net/project/showfiles.php?group_id=56262">download everything from here</a></b>, and you can get an overview of all the rules at the <a href="rules/index.html">rulesets index</a> page.</p> <p>PMD is <a href="integrations.html">integrated</a> with JDeveloper, Eclipse, JEdit, JBuilder, BlueJ, CodeGuide, NetBeans/Sun Java Studio Enterprise/Creator, IntelliJ IDEA, TextPad, Maven, Ant, Gel, JCreator, and Emacs.</p>

Java Prolog Interface (JPI) is an Application Provider Interface (API) for interaction between Java and Prolog programming languages. Is a bidirectional interface that communicate Java applications with Prolog program or database and Prolog procedures with Java class and methods. JPI is an abstraction layer over concrete prolog drivers over Prolog Engines. This API define all mechanism to interact with any Prolog Engine and maintain the application independent to a specific underlying engine. JPI have several connectors to open source prolog engines like SWI, YAP, XSB native engines and tuProlog, jTrolog, jLog Java based prolog engines. JPI study all related Java-Prolog integration libraries and take the betters features from each solution with the propose to achieve a common integration interface. The last feature allows switch the under laying Prolog Engine driver and the application code still be the same. JPI run over any Java Virtual Machine that support Java SE 5 or above. The project was tested over HotSpot, Open J9 and JRockit Virtual Machines over Operating Systems like Windows (7,8,10), Linux (Debian, Ubuntu) and Mac OS X. Can be deployed on Servlets Containers like Jetty, Tomcat or Glassfish Application Server. JPI can be include in any Java Project using the commonest Java Integration Development Enviroment (IDE) like Eclipse, Netbeans, IntelijIDEA and so on. JPI is developed and maintained by Prolobjectlink Project an open source initiative for build logic based applications using Prolog like fundamental Logic Programming Language in the persistence layer and application programming.

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.

Performs a grid search of parameter pairs for the a classifier (Y-axis, default is LinearRegression with the "Ridge" parameter) and the PLSFilter (X-axis, "# of Components") and chooses the best pair found for the actual predicting. The initial grid is worked on with 2-fold CV to determine the values of the parameter pairs for the selected type of evaluation (e.g., accuracy). The best point in the grid is then taken and a 10-fold CV is performed with the adjacent parameter pairs. If a better pair is found, then this will act as new center and another 10-fold CV will be performed (kind of hill-climbing). This process is repeated until no better pair is found or the best pair is on the border of the grid. In case the best pair is on the border, one can let GridSearch automatically extend the grid and continue the search. Check out the properties 'gridIsExtendable' (option '-extend-grid') and 'maxGridExtensions' (option '-max-grid-extensions <num>'). GridSearch can handle doubles, integers (values are just cast to int) and booleans (0 is false, otherwise true). float, char and long are supported as well. The best filter/classifier setup can be accessed after the buildClassifier call via the getBestFilter/getBestClassifier methods. Note on the implementation: after the data has been passed through the filter, a default NumericCleaner filter is applied to the data in order to avoid numbers that are getting too small and might produce NaNs in other schemes.

XApi is a dependency injection, service-oriented, polyglot java framework. It is comprised of standalone modules for use in web, desktop and mobile java applications. This codebase enables complete reuse of all code across your java stack, as well as the ability to easily define your own Platform, so you can build your own customized runtime environment to extend or replace existing platforms. It is currently geared towards servlets and gwt clients, but the target we are currently developing include: java, gwt [webkit, firefox, ie], javafx, playn [android, ios, flash], appengine & vert.x For Google Web Toolkit, we also include reflection support, more complete emulation of java.lang.Class, zero-overhead dependency injection, a range of code generation utilities, and even "magic-method injection" (for production compiles any method in the app can be swapped out with others based on configuration properties, or any AST mangling / code generating methods you wish to write). There is also a bytecode transformer in the works which will replace calls into the DI utility with direct references to static final factories, to allow the java compiler to completely erase all injection overhead in all java runtimes.

# pact-jvm-consumer-java8 Provides a Java8 lambda based DSL for use with Junit to build consumer tests. # A Lambda DSL for Pact This is an extension for the pact DSL provided by [pact-jvm-consumer](../pact-jvm-consumer). The difference between the default pact DSL and this lambda DSL is, as the name suggests, the usage of lambdas. The use of lambdas makes the code much cleaner. ## Why a new DSL implementation? The lambda DSL solves the following two main issues. Both are visible in the following code sample: ```java new PactDslJsonArray() .array() # open an array .stringValue(&quot;a1&quot;) # choose the method that is valid for arrays .stringValue(&quot;a2&quot;) # choose the method that is valid for arrays .closeArray() # close the array .array() # open an array .numberValue(1) # choose the method that is valid for arrays .numberValue(2) # choose the method that is valid for arrays .closeArray() # close the array .array() # open an array .object() # now we work with an object .stringValue(&quot;foo&quot;, &quot;Foo&quot;) # choose the method that is valid for objects .closeObject() # close the object and we&apos;re back in the array .closeArray() # close the array ``` ### The existing DSL is quite error-prone Methods may only be called in certain states. For example `object()` may only be called when you&apos;re currently working on an array whereas `object(name)` is only allowed to be called when working on an object. But both of the methods are available. You&apos;ll find out at runtime if you&apos;re using the correct method. Finally, the need for opening and closing objects and arrays makes usage cumbersome. The lambda DSL has no ambiguous methods and there&apos;s no need to close objects and arrays as all the work on such an object is wrapped in a lamda call. ### The existing DSL is hard to read When formatting your source code with an IDE the code becomes hard to read as there&apos;s no indentation possible. Of course, you could do it by hand but we want auto formatting! Auto formatting works great for the new DSL! ```java array.object((o) -&gt; { o.stringValue(&quot;foo&quot;, &quot;Foo&quot;); # an attribute o.stringValue(&quot;bar&quot;, &quot;Bar&quot;); # an attribute o.object(&quot;tar&quot;, (tarObject) -&gt; { # an attribute with a nested object tarObject.stringValue(&quot;a&quot;, &quot;A&quot;); # attribute of the nested object tarObject.stringValue(&quot;b&quot;, &quot;B&quot;); # attribute of the nested object }) }); ``` ## Installation ### Maven ``` &lt;dependency&gt; &lt;groupId&gt;au.com.dius&lt;/groupId&gt; &lt;artifactId&gt;pact-jvm-consumer-java8_2.12&lt;/artifactId&gt; &lt;version&gt;${pact.version}&lt;/version&gt; &lt;/dependency&gt; ``` ## Usage Start with a static import of `LambdaDsl`. This class contains factory methods for the lambda dsl extension. When you come accross the `body()` method of `PactDslWithProvider` builder start using the new extensions. The call to `LambdaDsl` replaces the call to instance `new PactDslJsonArray()` and `new PactDslJsonBody()` of the pact library. ```java io.pactfoundation.consumer.dsl.LambdaDsl.* ``` ### Response body as json array ```java import static io.pactfoundation.consumer.dsl.LambdaDsl.newJsonArray; ... PactDslWithProvider builder = ... builder.given(&quot;some state&quot;) .uponReceiving(&quot;a request&quot;) .path(&quot;/my-app/my-service&quot;) .method(&quot;GET&quot;) .willRespondWith() .status(200) .body(newJsonArray((a) -&gt; { a.stringValue(&quot;a1&quot;); a.stringValue(&quot;a2&quot;); }).build()); ``` ### Response body as json object ```java import static io.pactfoundation.consumer.dsl.LambdaDsl.newJsonBody; ... PactDslWithProvider builder = ... builder.given(&quot;some state&quot;) .uponReceiving(&quot;a request&quot;) .path(&quot;/my-app/my-service&quot;) .method(&quot;GET&quot;) .willRespondWith() .status(200) .body(newJsonBody((o) -&gt; { o.stringValue(&quot;foo&quot;, &quot;Foo&quot;); o.stringValue(&quot;bar&quot;, &quot;Bar&quot;); }).build()); ``` ### Examples #### Simple Json object When creating simple json structures the difference between the two approaches isn&apos;t big. ##### JSON ```json { &quot;bar&quot;: &quot;Bar&quot;, &quot;foo&quot;: &quot;Foo&quot; } ``` ##### Pact DSL ```java new PactDslJsonBody() .stringValue(&quot;foo&quot;, &quot;Foo&quot;) .stringValue(&quot;bar&quot;, &quot;Bar&quot;) ``` ##### Lambda DSL ```java newJsonBody((o) -&gt; { o.stringValue(&quot;foo&quot;, &quot;Foo&quot;); o.stringValue(&quot;bar&quot;, &quot;Bar&quot;); }).build(); ``` #### An array of arrays When we come to more complex constructs with arrays and nested objects the beauty of lambdas become visible! ##### JSON ```json [ [&quot;a1&quot;, &quot;a2&quot;], [1, 2], [{&quot;foo&quot;: &quot;Foo&quot;}] ] ``` ##### Pact DSL ```java new PactDslJsonArray() .array() .stringValue(&quot;a1&quot;) .stringValue(&quot;a2&quot;) .closeArray() .array() .numberValue(1) .numberValue(2) .closeArray() .array() .object() .stringValue(&quot;foo&quot;, &quot;Foo&quot;) .closeObject() .closeArray(); ``` ##### Lambda DSL ```java newJsonArray((rootArray) -&gt; { rootArray.array((a) -&gt; a.stringValue(&quot;a1&quot;).stringValue(&quot;a2&quot;)); rootArray.array((a) -&gt; a.numberValue(1).numberValue(2)); rootArray.array((a) -&gt; a.object((o) -&gt; o.stringValue(&quot;foo&quot;, &quot;Foo&quot;))); }).build(); ``` `object` is a reserved word in Kotlin. To allow using the DSL without escaping, a Kotlin extension `newObject` is available: ```kotlin newJsonArray { rootArray -&gt; rootArray.array { a -&gt; a.stringValue(&quot;a1&quot;).stringValue(&quot;a2&quot;) } rootArray.array { a -&gt; a.numberValue(1).numberValue(2) } rootArray.array { a -&gt; a.newObject { o -&gt; o.stringValue(&quot;foo&quot;, &quot;Foo&quot;) } } }.build(); ```