Download JAR files tagged by reduce with all dependencies

Smart Commit JDBC Driver enables 'smartcommit' mode for all JDBC drivers. In Smart Commit mode a JDBC connection will execute all queries in autocommit mode until it encounters a write operations. From the first write operation until the following commit or rollback, the connection will operate in transactional mode. It will then switch back to autocommit and remain in autocommit until it once again encounters a write operation. Using a JDBC connection in Smart Commit mode will reduce the number of read locks needed. Operating a JDBC connection in Smart Commit mode effectively reduces the isolation level of a transaction to READ_COMMITTED for all queries that are executed *BEFORE* a write operation in a transaction. All queries that are executed *AFTER* a write operation in a transaction, will be executed using the configured isolation level of the JDBC connection.

This is the parent module for all components of the Djenni framework - a test data generation framework that sets out to introduce better practices in test data management and generation. The core goals are: - to reduce the amount of time developers and testers spend manually crafting data - to make tests clearer by only focusing on the key data elements - to make tests even more complete by fuzzing data elements that are not deemed key to the test - to better support later test phases by helping developers to provide data generators suitable for large scale data generation in a thread safe / multi-threadable manner.

This small subproject regroup most of the stuff needed to build pmd. As some java code has been produced to reduce the use of scripts when generating the website, the build also requires a small jar, which is generated by this pom. IMPORTANT NOTE: the pmd-build.jar does not need to be recompile if you modify any ant or xslt files. You can alter the files that are in the xslt directory and directly uses the appropriate ant or java task. pmd-build should be recompiled and package (and its version incrememented) only if you modify the java code.

This small subproject regroup most of the stuff needed to build pmd. As some java code has been produced to reduce the use of scripts when generating the website, the build also requires a small jar, which is generated by this pom. IMPORTANT NOTE: the pmd-build.jar does not need to be recompile if you modify any ant or xslt files. You can alter the files that are in the xslt directory and directly uses the appropriate ant or java task. pmd-build should be recompiled and package (and its version incrememented) only if you modify the java code.

"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.

Scala classes to allow more elegant use of Multiverse from Scala. Atm the functionality is quite limited since not a lot of effort was put in the Scala integration. For the 0.6 release Multiverse should be able to work with in Scala written transactional objects configured with the Multiverse annotations. So that would reduce the need for this library, although in Scala they want to have special Scala 'interfaces' that provide some syntactic sugar to use java collections in Scala. So this module would be the good location for that. If anyone would like to help improving the Multiverse/Scala integration, please don't hesitate to join.

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.

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.