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Xypron Statistics is a Java library which was developped with supply chain simulation in mind. The normal, the exponential and the gamma distribution have been included. Methods to calculate fill rate and order rate service levels as well as safety factors are provided. The Mersenne Twister algorithm is used to provide high quality random number generation. Some functions for the gamma distribution where adopted from http://www.ssfnet.org/download/ssfnet_raceway-2.0.tar.gz. For these the following applies: Copyright 1999 CERN - European Organization for Nuclear Research. Permission to use, copy, modify, distribute and sell this software and its documentation for any purpose is hereby granted without fee, provided that the above copyright notice appear in all copies and that both that copyright notice and this permission notice appear in supporting documentation. CERN makes no representations about the suitability of this software for any purpose. It is provided "as is" without expressed or implied warranty.

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.

JUNG the Java Universal Network/Graph Framework--is a software library that provides a common and extensible language for the modeling, analysis, and visualization of data that can be represented as a graph or network. It is written in Java, which allows JUNG-based applications to make use of the extensive built-in capabilities of the Java API, as well as those of other existing third-party Java libraries. The JUNG architecture is designed to support a variety of representations of entities and their relations, such as directed and undirected graphs, multi-modal graphs, graphs with parallel edges, and hypergraphs. It provides a mechanism for annotating graphs, entities, and relations with metadata. This facilitates the creation of analytic tools for complex data sets that can examine the relations between entities as well as the metadata attached to each entity and relation. The current distribution of JUNG includes implementations of a number of algorithms from graph theory, data mining, and social network analysis, such as routines for clustering, decomposition, optimization, random graph generation, statistical analysis, and calculation of network distances, flows, and importance measures (centrality, PageRank, HITS, etc.). JUNG also provides a visualization framework that makes it easy to construct tools for the interactive exploration of network data. Users can use one of the layout algorithms provided, or use the framework to create their own custom layouts. In addition, filtering mechanisms are provided which allow users to focus their attention, or their algorithms, on specific portions of the graph.

JUNG the Java Universal Network/Graph Framework--is a software library that provides a common and extendible language for the modeling, analysis, and visualization of data that can be represented as a graph or network. It is written in Java, which allows JUNG-based applications to make use of the extensive built-in capabilities of the Java API, as well as those of other existing third-party Java libraries. The JUNG architecture is designed to support a variety of representations of entities and their relations, such as directed and undirected graphs, multi-modal graphs, graphs with parallel edges, and hypergraphs. It provides a mechanism for annotating graphs, entities, and relations with metadata. This facilitates the creation of analytic tools for complex data sets that can examine the relations between entities as well as the metadata attached to each entity and relation. The current distribution of JUNG includes implementations of a number of algorithms from graph theory, data mining, and social network analysis, such as routines for clustering, decomposition, optimization, random graph generation, statistical analysis, and calculation of network distances, flows, and importance measures (centrality, PageRank, HITS, etc.). JUNG also provides a visualization framework that makes it easy to construct tools for the interactive exploration of network data. Users can use one of the layout algorithms provided, or use the framework to create their own custom layouts. In addition, filtering mechanisms are provided which allow users to focus their attention, or their algorithms, on specific portions of the graph.

This package currently contains classes for training multilayer perceptrons with one hidden layer, where the number of hidden units is user specified. MLPClassifier can be used for classification problems and MLPRegressor is the corresponding class for numeric prediction tasks. The former has as many output units as there are classes, the latter only one output unit. Both minimise a penalised squared error with a quadratic penalty on the (non-bias) weights, i.e., they implement "weight decay", where this penalised error is averaged over all training instances. The size of the penalty can be determined by the user by modifying the "ridge" parameter to control overfitting. The sum of squared weights is multiplied by this parameter before added to the squared error. Both classes use BFGS optimisation by default to find parameters that correspond to a local minimum of the error function. but optionally conjugated gradient descent is available, which can be faster for problems with many parameters. Logistic functions are used as the activation functions for all units apart from the output unit in MLPRegressor, which employs the identity function. Input attributes are standardised to zero mean and unit variance. MLPRegressor also rescales the target attribute (i.e., "class") using standardisation. All network parameters are initialised with small normally distributed random values.

JUNG the Java Universal Network/Graph Framework--is a software library that provides a common and extendible language for the modeling, analysis, and visualization of data that can be represented as a graph or network. It is written in Java, which allows JUNG-based applications to make use of the extensive built-in capabilities of the Java API, as well as those of other existing third-party Java libraries. The JUNG architecture is designed to support a variety of representations of entities and their relations, such as directed and undirected graphs, multi-modal graphs, graphs with parallel edges, and hypergraphs. It provides a mechanism for annotating graphs, entities, and relations with metadata. This facilitates the creation of analytic tools for complex data sets that can examine the relations between entities as well as the metadata attached to each entity and relation. The current distribution of JUNG includes implementations of a number of algorithms from graph theory, data mining, and social network analysis, such as routines for clustering, decomposition, optimization, random graph generation, statistical analysis, and calculation of network distances, flows, and importance measures (centrality, PageRank, HITS, etc.). JUNG also provides a visualization framework that makes it easy to construct tools for the interactive exploration of network data. Users can use one of the layout algorithms provided, or use the framework to create their own custom layouts. In addition, filtering mechanisms are provided which allow users to focus their attention, or their algorithms, on specific portions of the graph.

JUNG the Java Universal Network/Graph Framework--is a software library that provides a common and extendible language for the modeling, analysis, and visualization of data that can be represented as a graph or network. It is written in Java, which allows JUNG-based applications to make use of the extensive built-in capabilities of the Java API, as well as those of other existing third-party Java libraries. The JUNG architecture is designed to support a variety of representations of entities and their relations, such as directed and undirected graphs, multi-modal graphs, graphs with parallel edges, and hypergraphs. It provides a mechanism for annotating graphs, entities, and relations with metadata. This facilitates the creation of analytic tools for complex data sets that can examine the relations between entities as well as the metadata attached to each entity and relation. The current distribution of JUNG includes implementations of a number of algorithms from graph theory, data mining, and social network analysis, such as routines for clustering, decomposition, optimization, random graph generation, statistical analysis, and calculation of network distances, flows, and importance measures (centrality, PageRank, HITS, etc.). JUNG also provides a visualization framework that makes it easy to construct tools for the interactive exploration of network data. Users can use one of the layout algorithms provided, or use the framework to create their own custom layouts. In addition, filtering mechanisms are provided which allow users to focus their attention, or their algorithms, on specific portions of the graph.

# 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(); } ```

# 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 [junit provider support](pact-jvm-provider-junit) 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(); } ```

Chips-n-Salsa is a Java library of customizable, hybridizable, iterative, parallel, stochastic, and self-adaptive local search algorithms. The library includes implementations of several stochastic local search algorithms, including simulated annealing, hill climbers, as well as constructive search algorithms such as stochastic sampling. Chips-n-Salsa now also includes genetic algorithms as well as evolutionary algorithms more generally. The library very extensively supports simulated annealing. It includes several classes for representing solutions to a variety of optimization problems. For example, the library includes a BitVector class that implements vectors of bits, as well as classes for representing solutions to problems where we are searching for an optimal vector of integers or reals. For each of the built-in representations, the library provides the most common mutation operators for generating random neighbors of candidate solutions, as well as common crossover operators for use with evolutionary algorithms. Additionally, the library provides extensive support for permutation optimization problems, including implementations of many different mutation operators for permutations, and utilizing the efficiently implemented Permutation class of the JavaPermutationTools (JPT) library. Chips-n-Salsa is customizable, making extensive use of Java's generic types, enabling using the library to optimize other types of representations beyond what is provided in the library. It is hybridizable, providing support for integrating multiple forms of local search (e.g., using a hill climber on a solution generated by simulated annealing), creating hybrid mutation operators (e.g., local search using multiple mutation operators), as well as support for running more than one type of search for the same problem concurrently using multiple threads as a form of algorithm portfolio. Chips-n-Salsa is iterative, with support for multistart metaheuristics, including implementations of several restart schedules for varying the run lengths across the restarts. It also supports parallel execution of multiple instances of the same, or different, stochastic local search algorithms for an instance of a problem to accelerate the search process. The library supports self-adaptive search in a variety of ways, such as including implementations of adaptive annealing schedules for simulated annealing, such as the Modified Lam schedule, implementations of the simpler annealing schedules but which self-tune the initial temperature and other parameters, and restart schedules that adapt to run length.