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EasyConfig provides simple way to overview and apply settings to file or folder based collections of files. Synonyms to "setting" are property, attribute, value while throughout application "setting" is used. The settings are groupped in "configuration" that is collection of settings from various sources. Main design concepts are: * minimalistic way to describe configuration * pluggable support for data types (validation), setting sources, source handlers Sample use case: An application is deployed in multiple locations. We need to quickly check key settings/parameters and optionally modify some of them. These values are located in different places: - in files directly in file structure - in files inside archive files (optionally nested archives) - values in DB tables - values accessible via URLs - other sources (just guessed: SSH/telnet connection+some command(s), UPnP devices, proprietary protocols, etc) We gather info from any supported (extendable) source and can modify and apply changes if supported by source (e.g. we can't update value that is count of rows in DB table, but we can read that value).

Mahout's goal is to build scalable machine learning libraries. With scalable we mean: Scalable to reasonably large data sets. Our core algorithms for clustering, classification and batch based collaborative filtering are implemented on top of Apache Hadoop using the map/reduce paradigm. However we do not restrict contributions to Hadoop based implementations: Contributions that run on a single node or on a non-Hadoop cluster are welcome as well. The core libraries are highly optimized to allow for good performance also for non-distributed algorithms. Scalable to support your business case. Mahout is distributed under a commercially friendly Apache Software license. Scalable community. The goal of Mahout is to build a vibrant, responsive, diverse community to facilitate discussions not only on the project itself but also on potential use cases. Come to the mailing lists to find out more. Currently Mahout supports mainly four use cases: Recommendation mining takes users' behavior and from that tries to find items users might like. Clustering takes e.g. text documents and groups them into groups of topically related documents. Classification learns from existing categorized documents what documents of a specific category look like and is able to assign unlabelled documents to the (hopefully) correct category. Frequent itemset mining takes a set of item groups (terms in a query session, shopping cart content) and identifies, which individual items usually appear together.

This module contains a more general approach to construct AlignmentAlgorithms by relying on the theoretical concept of Algebraic Dynamic Programming (ADP) as developed by Giegerich et al. ADP defines four ingredients for an alignment algorithm: 1.) A signature that defines the permitted alignment operations. Operations are just function templates with an associated arity, meaning the number of arguments it takes from the left sequence and from the right sequence. In the TCSAlignmentToolbox we have a fixed signature with the following operations: REPLACEMENT(1, 1), DELETION(1, 0), INSERTION(0, 1), SKIPDELETION(1, 0) and SKIPINSERTION(0, 1) 2.) A regular tree grammar that produces alignments, that is: sequences of operations, in a restricted fashion. 3.) An algebra that can translate such trees to a cost. In the TCSAlignmentToolbox this is a Comparator. 4.) A choice function, in case of the TCSAlignmentToolbox: the strict minimum or the soft minimum. An alignment algorithm in the TCSAlignmentToolbox sense of the word then is the combination of choice function and grammar. While we provide hardcoded versions of these combinations in the main package, the adp package allows you to create your own grammars. You can combine them with a choice function by instantiating one of the Algorithm classes provided in this package with a grammar of your choice. For example: AlignmentAlgorithm algo = new SoftADPScoreAlgorithm(my_grammar, comparator); creates an alignment algorithm that implicitly produces all possible alignments your grammar can construct with the given input, translates them to a cost using the algebra/comparator you provided and applies the soft minimum to return the score. This all gets efficient by dynamic programming. Note that there is runtime overhead when using this method in comparison with the hardcoded algorithms. But for complicated grammars this is a much easier way to go. For more information on the theory, please refer to my master's thesis: "Adaptive Affine Sequence Alignment using Algebraic Dynamic Programming"

pact-jvm-consumer-groovy-v3 =========================== Groovy DSL for Pact JVM implementing V3 specification changes. ##Dependency The library is available on maven central using: * group-id = `au.com.dius` * artifact-id = `pact-jvm-consumer-groovy-v3_2.11` * version-id = `2.2.x` or `3.0.x` ##Usage Add the `pact-jvm-consumer-groovy-v3` library to your test class path. This provides a `PactMessageBuilder` class for you to use to define your pacts. If you are using gradle for your build, add it to your `build.gradle`: dependencies { testCompile 'au.com.dius:pact-jvm-consumer-groovy-v3_2.11:2.2.12' } ## Consumer test for a message consumer The `PactMessageBuilder` class provides a DSL for defining your message expectations. It works in much the same way as the `PactBuilder` class for Request-Response interactions. ### Step 1 - define the message expectations Create a test that uses the `PactMessageBuilder` to define a message expectation, and then call `run`. This will invoke the given closure with a message for each one defined in the pact. ```groovy def eventStream = new PactMessageBuilder().call { serviceConsumer 'messageConsumer' hasPactWith 'messageProducer' given 'order with id 10000004 exists' expectsToReceive 'an order confirmation message' withMetaData(type: 'OrderConfirmed') // Can define any key-value pairs here withContent(contentType: 'application/json') { type 'OrderConfirmed' audit { userCode 'messageService' } origin 'message-service' referenceId '10000004-2' timeSent: '2015-07-22T10:14:28+00:00' value { orderId '10000004' value '10.000000' fee '10.00' gst '15.00' } } } ``` ### Step 2 - call your message handler with the generated messages This example tests a message handler that gets messages from a Kafka topic. In this case the Pact message is wrapped as a Kafka `MessageAndMetadata`. ```groovy eventStream.run { Message message -> messageHandler.handleMessage(new MessageAndMetadata('topic', 1, new kafka.message.Message(message.contentsAsBytes()), 0, null, valueDecoder)) } ``` ### Step 3 - validate that the message was handled correctly ```groovy def order = orderRepository.getOrder('10000004') assert order.status == 'confirmed' assert order.value == 10.0 ``` ### Step 4 - Publish the pact file If the test was successful, a pact file would have been produced with the message from step 1.

pact-jvm-consumer-groovy-v3 =========================== Groovy DSL for Pact JVM implementing V3 specification changes. ##Dependency The library is available on maven central using: * group-id = `au.com.dius` * artifact-id = `pact-jvm-consumer-groovy-v3_2.11` * version-id = `2.2.x` or `3.0.x` ##Usage Add the `pact-jvm-consumer-groovy-v3` library to your test class path. This provides a `PactMessageBuilder` class for you to use to define your pacts. If you are using gradle for your build, add it to your `build.gradle`: dependencies { testCompile 'au.com.dius:pact-jvm-consumer-groovy-v3_2.11:2.2.12' } ## Consumer test for a message consumer The `PactMessageBuilder` class provides a DSL for defining your message expectations. It works in much the same way as the `PactBuilder` class for Request-Response interactions. ### Step 1 - define the message expectations Create a test that uses the `PactMessageBuilder` to define a message expectation, and then call `run`. This will invoke the given closure with a message for each one defined in the pact. ```groovy def eventStream = new PactMessageBuilder().call { serviceConsumer 'messageConsumer' hasPactWith 'messageProducer' given 'order with id 10000004 exists' expectsToReceive 'an order confirmation message' withMetaData(type: 'OrderConfirmed') // Can define any key-value pairs here withContent(contentType: 'application/json') { type 'OrderConfirmed' audit { userCode 'messageService' } origin 'message-service' referenceId '10000004-2' timeSent: '2015-07-22T10:14:28+00:00' value { orderId '10000004' value '10.000000' fee '10.00' gst '15.00' } } } ``` ### Step 2 - call your message handler with the generated messages This example tests a message handler that gets messages from a Kafka topic. In this case the Pact message is wrapped as a Kafka `MessageAndMetadata`. ```groovy eventStream.run { Message message -> messageHandler.handleMessage(new MessageAndMetadata('topic', 1, new kafka.message.Message(message.contentsAsBytes()), 0, null, valueDecoder)) } ``` ### Step 3 - validate that the message was handled correctly ```groovy def order = orderRepository.getOrder('10000004') assert order.status == 'confirmed' assert order.value == 10.0 ``` ### Step 4 - Publish the pact file If the test was successful, a pact file would have been produced with the message from step 1.

minitest provides a complete suite of testing facilities supporting TDD, BDD, mocking, and benchmarking. "I had a class with Jim Weirich on testing last week and we were allowed to choose our testing frameworks. Kirk Haines and I were paired up and we cracked open the code for a few test frameworks... I MUST say that minitest is *very* readable / understandable compared to the 'other two' options we looked at. Nicely done and thank you for helping us keep our mental sanity." -- Wayne E. Seguin minitest/unit is a small and incredibly fast unit testing framework. It provides a rich set of assertions to make your tests clean and readable. minitest/spec is a functionally complete spec engine. It hooks onto minitest/unit and seamlessly bridges test assertions over to spec expectations. minitest/benchmark is an awesome way to assert the performance of your algorithms in a repeatable manner. Now you can assert that your newb co-worker doesn't replace your linear algorithm with an exponential one! minitest/mock by Steven Baker, is a beautifully tiny mock (and stub) object framework. minitest/pride shows pride in testing and adds coloring to your test output. I guess it is an example of how to write IO pipes too. :P minitest/unit is meant to have a clean implementation for language implementors that need a minimal set of methods to bootstrap a working test suite. For example, there is no magic involved for test-case discovery. "Again, I can't praise enough the idea of a testing/specing framework that I can actually read in full in one sitting!" -- Piotr Szotkowski Comparing to rspec: rspec is a testing DSL. minitest is ruby. -- Adam Hawkins, "Bow Before MiniTest" minitest doesn't reinvent anything that ruby already provides, like: classes, modules, inheritance, methods. This means you only have to learn ruby to use minitest and all of your regular OO practices like extract-method refactorings still apply.

BEhavioural Agents Simple Testing Tool - BEAST Tool The aim of this project is the development of a system which allows Behavior Driven Development (BDD) in Multi-Agent Systems (MAS), to make testing practices more accessible and intuitive to everybody. In one hand, in order to let tests be writable by newcomers and experts alike, system must allow the redaction of tests in plain text, because client does not need to have knowledge of our code. This plain text will be traduced to software later. The definition of test will be realized with the terminology Given-When-Then, which allows trace an easy guide of the behavior of a given scenario when something happened. In the other hand, due to the complexity of MAS, making unit testing of an agent that needs the interaction with others is almost impossible until the whole system is finished. This implies to leave testing issues to the end of the project, generating big troubles in case of malfunction. Consequently, its necessary to carry out a tool to allow the creation of mock agents and to perform tests during the whole development process. Therefore another objective of our systems is to include a mocking tool which permits testing continuously. Definitively, our tool allows the testing of any MAS in the development process, increasing its modularity and decreasing its elaboration and testing cost. These tests will be written in plain text so that anyone would be able to understand them. For further reading, a paper published in ITMAS2012 workshop can be found in: http://scholar.google.es/citations?view_op=view_citation&hl=es&user=mT3KgXUAAAAJ&citation_for_view=mT3KgXUAAAAJ:Tyk-4Ss8FVUC

Mahout's goal is to build scalable machine learning libraries. With scalable we mean: Scalable to reasonably large data sets. Our core algorithms for clustering, classfication and batch based collaborative filtering are implemented on top of Apache Hadoop using the map/reduce paradigm. However we do not restrict contributions to Hadoop based implementations: Contributions that run on a single node or on a non-Hadoop cluster are welcome as well. The core libraries are highly optimized to allow for good performance also for non-distributed algorithms. Scalable to support your business case. Mahout is distributed under a commercially friendly Apache Software license. Scalable community. The goal of Mahout is to build a vibrant, responsive, diverse community to facilitate discussions not only on the project itself but also on potential use cases. Come to the mailing lists to find out more. Currently Mahout supports mainly four use cases: Recommendation mining takes users' behavior and from that tries to find items users might like. Clustering takes e.g. text documents and groups them into groups of topically related documents. Classification learns from exisiting categorized documents what documents of a specific category look like and is able to assign unlabelled documents to the (hopefully) correct category. Frequent itemset mining takes a set of item groups (terms in a query session, shopping cart content) and identifies, which individual items usually appear together.

# Pact Spring/JUnit runner ## Overview Library provides ability to play contract tests against a provider using Spring & JUnit. This library is based on and references the JUnit package, so see the [Pact JUnit 4](../pact-jvm-provider-junit) or [Pact JUnit 5](../pact-jvm-provider-junit5) providers for more details regarding configuration using JUnit. Supports: - Standard ways to load pacts from folders and broker - Easy way to change assertion strategy - Spring Test MockMVC Controllers and ControllerAdvice using MockMvc standalone setup. - MockMvc debugger output - Multiple @State runs to test a particular Provider State multiple times - **au.com.dius.pact.provider.junit.State** custom annotation - before each interaction that requires a state change, all methods annotated by `@State` with appropriate the state listed will be invoked. **NOTE:** For publishing provider verification results to a pact broker, make sure the Java system property `pact.provider.version` is set with the version of your provider. ## Example of MockMvc test ```java @RunWith(RestPactRunner.class) // Custom pact runner, child of PactRunner which runs only REST tests @Provider("myAwesomeService") // Set up name of tested provider @PactFolder("pacts") // Point where to find pacts (See also section Pacts source in documentation) public class ContractTest { //Create an instance of your controller. We cannot autowire this as we're not using (and don't want to use) a Spring test runner. @InjectMocks private AwesomeController awesomeController = new AwesomeController(); //Mock your service logic class. We'll use this to create scenarios for respective provider states. @Mock private AwesomeBusinessLogic awesomeBusinessLogic; //Create an instance of your controller advice (if you have one). This will be passed to the MockMvcTarget constructor to be wired up with MockMvc. @InjectMocks private AwesomeControllerAdvice awesomeControllerAdvice = new AwesomeControllerAdvice(); //Create a new instance of the MockMvcTarget and annotate it as the TestTarget for PactRunner @TestTarget public final MockMvcTarget target = new MockMvcTarget(); @Before //Method will be run before each test of interaction public void before() { //initialize your mocks using your mocking framework MockitoAnnotations.initMocks(this); //configure the MockMvcTarget with your controller and controller advice target.setControllers(awesomeController); target.setControllerAdvice(awesomeControllerAdvice); } @State("default", "no-data") // Method will be run before testing interactions that require "default" or "no-data" state public void toDefaultState() { target.setRunTimes(3); //let's loop through this state a few times for a 3 data variants when(awesomeBusinessLogic.getById(any(UUID.class))) .thenReturn(myTestHelper.generateRandomReturnData(UUID.randomUUID(), ExampleEnum.ONE)) .thenReturn(myTestHelper.generateRandomReturnData(UUID.randomUUID(), ExampleEnum.TWO)) .thenReturn(myTestHelper.generateRandomReturnData(UUID.randomUUID(), ExampleEnum.THREE)); } @State("error-case") public void SingleUploadExistsState_Success() { target.setRunTimes(1); //tell the runner to only loop one time for this state //you might want to throw exceptions to be picked off by your controller advice when(awesomeBusinessLogic.getById(any(UUID.class))) .then(i -> { throw new NotCoolException(i.getArgumentAt(0, UUID.class).toString()); }); } } ``` ## Using a Spring runner (version 3.5.7+) You can use `SpringRestPactRunner` instead of the default Pact runner to use the Spring test annotations. This will allow you to inject or mock spring beans. For example: ```java @RunWith(SpringRestPactRunner.class) @Provider("pricing") @PactBroker(protocol = "https", host = "${pactBrokerHost}", port = "443", authentication = @PactBrokerAuth(username = "${pactBrokerUser}", password = "${pactBrokerPassword}")) @SpringBootTest(webEnvironment = SpringBootTest.WebEnvironment.DEFINED_PORT) public class PricingServiceProviderPactTest { @MockBean private ProductClient productClient; // This will replace the bean with a mock in the application context @TestTarget @SuppressWarnings(value = "VisibilityModifier") public final Target target = new HttpTarget(8091); @State("Product X010000021 exists") public void setupProductX010000021() throws IOException { reset(productClient); ProductBuilder product = new ProductBuilder() .withProductCode("X010000021"); when(productClient.fetch((Set<String>) argThat(contains("X010000021")), any())).thenReturn(product); } @State("the product code X00001 can be priced") public void theProductCodeX00001CanBePriced() throws IOException { reset(productClient); ProductBuilder product = new ProductBuilder() .withProductCode("X00001"); when(productClient.find((Set<String>) argThat(contains("X00001")), any())).thenReturn(product); } } ``` ### Using Spring Context Properties (version 3.5.14+) From version 3.5.14 onwards, the SpringRestPactRunner will look up any annotation expressions (like `${pactBrokerHost}`) above) from the Spring context. For Springboot, this will allow you to define the properties in the application test properties. For instance, if you create the following `application.yml` in the test resources: ```yaml pactbroker: host: "your.broker.local" port: "443" protocol: "https" auth: username: "<your broker username>" password: "<your broker password>" ``` Then you can use the defaults on the `@PactBroker` annotation. ```java @RunWith(SpringRestPactRunner.class) @Provider("My Service") @PactBroker( authentication = @PactBrokerAuth(username = "${pactbroker.auth.username}", password = "${pactbroker.auth.password}") ) @SpringBootTest(webEnvironment = SpringBootTest.WebEnvironment.RANDOM_PORT) public class PactVerificationTest { ``` ### Using a random port with a Springboot test (version 3.5.14+) If you use a random port in a springboot test (by setting `SpringBootTest.WebEnvironment.RANDOM_PORT`), you can use the `SpringBootHttpTarget` which will get the application port from the spring application context. For example: ```java @RunWith(SpringRestPactRunner.class) @Provider("My Service") @PactBroker @SpringBootTest(webEnvironment = SpringBootTest.WebEnvironment.RANDOM_PORT) public class PactVerificationTest { @TestTarget public final Target target = new SpringBootHttpTarget(); } ```