Download JAR files tagged by generated with all dependencies

Customizable cruft-free Scala archetype. Options: -sourceFolders: [all-in-src-java, scala-only, both-split-src]. DEFAULT: all-in-src-java. --all-in-src-java: Scala and Java code are both in src/main/java (same for test). Plays nicely with IDEs. --scala-only: only src/main/scala set up, and set as source folder. --both-split-src: both src/main/java and src/main/scala. Can play merry havock with IDEs, so not the default. -testLibrary: [scalatest, specs2, scalacheck-only, junit-only]. DEFAULT: scalatest. --scalatest: adds Scalatest AND Scalacheck AND JUnit for most of your testing needs. --specs2: adds specs2 AND Scalacheck AND JUnit. --scalacheck-junit: adds Scalacheck AND JUnit. --junit-only: adds JUnit ONLY. -scalaVersion: 2.10.x+. DEFAULT: 2.11.2. *The Scala tool version is generated automatically. *Some Scala versions may not be compatible with selected test library versions. Adjust as necessary.

This meta classifier creates a number of disjoint, stratified folds out of the data and feeds each chunk of data to a copy of the supplied base classifier. Predictions are made via majority vote, since all the generated base classifiers are put into the Vote meta classifier. Useful for base classifiers that are quadratic or worse in time behavior, regarding number of instances in the training data. For more information, see: Ting, K. M., Witten, I. H.: Stacking Bagged and Dagged Models. In: Fourteenth international Conference on Machine Learning, San Francisco, CA, 367-375, 1997.

This class implements a single conjunctive rule learner that can predict for numeric and nominal class labels. A rule consists of antecedents "AND"ed together and the consequent (class value) for the classification/regression. In this case, the consequent is the distribution of the available classes (or mean for a numeric value) in the dataset. If the test instance is not covered by this rule, then it's predicted using the default class distributions/value of the data not covered by the rule in the training data.This learner selects an antecedent by computing the Information Gain of each antecendent and prunes the generated rule using Reduced Error Prunning (REP) or simple pre-pruning based on the number of antecedents. For classification, the Information of one antecedent is the weighted average of the entropies of both the data covered and not covered by the rule. For regression, the Information is the weighted average of the mean-squared errors of both the data covered and not covered by the rule. In pruning, weighted average of the accuracy rates on the pruning data is used for classification while the weighted average of the mean-squared errors on the pruning data is used for regression.

A Maven Mojo to download and "degenerate" non-maven projects into sources and binaries This MOJO is supposed to scrape and "de-generate" a zip file containing jars into a project's generated-sources folder, skipping items provided in the `src/` folder. It is intended to "mavenize-with-changes" an existing project and allow the user (of the Mojo) to replace classes which they do not have the ability/desire to recompile without setting up a full build. It can also be used to import non-maven projects by not changing anything. The itch I wanted to scratch was mostly to port libGDX to Maven, while replacing the native methods on Matrix4, without rebuilding the whole project. In essence - it's an alternative to patching someone else's project.

The biblio transformation engine is a java framework developed by the Hellenic National Documentation Centre (EKT, www.ekt.gr) and consists of programmatic APIs for filtering and modifying records that are retrieved from various types of data sources (eg. databases, files, legacy data sources) as well as for outputing them in appropriate standards formats (eg. database files, txt, xml, Excel). The framework includes independent abstract modules that are executed seperately, offering in many cases alternative choices to the user depending of the input data set, the transformation workflow that needs to be executed and the output format that needs to be generated.

rapiddweller 'Benerator' is a software solution to generate, anonymize, pseudonymize and migrate data for development, testing and training purposes. rapiddweller 'Benerator' is easy to learn, integrates into CI Tools like Jenkins and Gitlab CI and allows to create data models on the most abstract level. rapiddweller 'Benerator' allows creating realistic and valid high-volume test data, used for testing (unit/integration/load) and showcase setup. Metadata constraints are imported from systems and/or configuration files. Data can imported from and exported to files and systems, anonymized or generated from scratch. Domain packages provide reusable generators for creating domain-specific data as names and addresses internationalizable in language and region. It is strongly customizable with plugins and configuration options. rapiddweller 'Benerator' is build for Java 1.8 and Java 11. The development is ongoing for Java 11.

Idaithalam is a low code Test automation Framework, developed using Java and Cucumber. It leverages Behavior Driven Development (BDD). Tester can create test cases/scripts in simple Excel with API Spec. Excel is a simplified way to create Json based test scripts in Idaithalam. Test cases can be created quickly and tested in minutes. As a tester, you need to create Excel and pass it to Idaithalam Framework. First, generate the Json based test scripts(Virtualan Collection) from Excel. During test execution, this test script collection can be directly utilized. Then it generates Feature files from the Virtualan Collection and its executed. Lastly, It generates test report in BDD/Cucumber style. This provide complete testing support for REST APIs, GraphQL, RDBMS DB and Kafka Event messages #idaithalam #apitesting #testautomation #restapitesting #insprintautomation #rdbmstesting #graphqltesting #kafkamessagetesting #eventvalidation #messagevalidation #cucumber #bdd Virtualan Collection is a JSON based TestScripts generated from Excel API testing can be automated using Excel, POSTMAN collection and VIRTUALAN - Utilized for API testing. - Utilized Contract testing. - Utilized Production Checkout. - Agile Testing - Progressive Regression can be done for each sprint. Reduce lot of manual effects.

Tool used to generate the preferred class information for downloadable JAR files in the form of a META-INF/PREFERRED.LIST required for use by the {@link net.jini.loader.pref.PreferredClassLoader}. The list is generated by examining the dependencies of classes contained within a target JAR file and zero or more additional supporting JAR files. Through various command-line options, a set of "root" classes are identified as belonging to a public API. These root classes provide the starting point for recursively computing a dependency graph, finding all of the classes referenced in the public API of the root classes, finding all of the classes referenced in turn by the public API of those classes, and so on, until no new classes are found. The results of the dependency analysis are combined with the preferred list information in the additional supporting JAR files to compute a preferred list having the smallest number of entries that describes the preferred state of the classes and resources contained in all of the JAR files. The output of the tool is a new version of the target JAR file containing the generated preferred list, and/or a copy of the list printed to System.out.

JCQL is a tool allowing to generate boilerplate java code from existing cassandra schema. It is intended to be used with Cassandra 2.1+ due to support of UDTs (User Defined Types)/Tuples/Collections. Cassandra's UDTs/Tuples/Collections and an ability to introspect schema through java driver make it possible to automatically generate POJOs and corresponding mappers between database and java models. Properly generated java code saves development efforts and is less error-prone compared to hand coding. Accompanied with proper CI and deployment it can also guarantee consistency between database and java models at any point of application lifecycle from development to production rollout. JCQL does not rely on java reflection or annotations which means all discrepancies between actual cassandra schema and what client code expects it to be will be identified during compilation not at runtime in the middle of the night right after production release. No need to worry about Cassandra client code performance implications due to use of reflection.

This is a specialized version of the Clirr Maven Plugin. It adds capabilities for excluding specific error types, as well as separating code into three, rather than two, subgroups: Internal code (no checks) Externally invoked code (Annotated with an "externally invoked" annotation, same as Externally implemented, but adding methods to interfaces and abstract classes is allowed) Externally implemented code (Assumed default. Full backwards compatibility required *unless* an interface is annotated with a defined adaptor annotation, in which case full backwards compatibility is required for the adaptor class, but the rules of @ExternallyInvoked apply to the interface itself) Clirr is a tool that checks Java libraries for binary and source compatibility with older releases. Basically you give it two sets of jar files and Clirr dumps out a list of changes in the public API. The clirr-maven-plugin can be configured to break the build, if it detects incompatible api changes. In a continuous integration process, the clirr-maven-plugin can automatically prevent accidental introduction of binary or source compatibility problems. Additionally, the plugin can generate a report as part of the generated site.