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Release Audit Tool (RAT) is a tool to improve accuracy and efficiency when checking releases. It is heuristic in nature: making guesses about possible problems. It will produce false positives and cannot find every possible issue with a release. It's reports require interpretation. In response to demands from project quality tool developers, RAT is available as a library suitable for inclusion in tools. This POM describes that library. Note that binary compatibility is not gauranteed between 0.x releases.

A tool to help visualise API differences between two different versions of a project. Jardiff takes two jar files and outputs all the public API changes as xml, html or plain text. Currently this is command line driven, though we are working on an ant plugin. It requires java 1.4 to run. <h2>Background</h2> At OSjava, we were using a shell script to work out API differences between our release versions. This unfortunately doesn't scale very well to larger projects, the main reason for jardiff was to do the same in a much shorter time (From memory it was taking minutes to get the API diff from two fairly small jars). In it's current state, jardiff can generate the api differences between rt.jar for java 1.4 and java 1.5 in under 20 seconds, so it should scale to even the largest of projects.

Apache Spatial Information System (SIS) is a free software, Java language library for developing geospatial applications. SIS provides data structures for geographic features and associated metadata along with methods to manipulate those data structures. The library is an implementation of GeoAPI 3.0.2 interfaces and can be used for desktop or server applications. The SIS metadata module forms the base of the library and enables the creation of metadata objects which comply with the ISO 19115 international standard. The SIS referencing module enable the construction of geodetic data structures for geospatial referencing as defined by ISO 19111 standard, along with the associated operations which enable the transformation of coordinates between different reference systems. The SIS storage modules provide a common approach to the reading and writing of metadata, features and coverages applicable to simple imagery as to many dimensional data structures.

Stepping is a framework designed to ease the implementation of data processing solutions. In use cases where we need to implement data or data-streaming algorithms or any other processing on data, we need to first handle many different infrastructure issues. For example, we need to decide how to split the data processing logic into different steps, think about our threading policy, how to handle communication between the different steps, error handling etc. One of the most important subjects is the Threading Policy of our solution. For example, we need to think how many threads to open, have the option to distribute the processing of data to multiple 'executors' in parallel, have a thread-safe communication layer between the threads etc. On top of that we also care a lot about the performance of our solution, we want to make sure that the latency added by these infrastructures is minimal as possible. Stepping aims to handle many of these aspects so developers can spend their time on the business logic instead of solving these infrastructure and data flow issues issues over and over again.

R can use RJava or jsr223 to communicate with java. R also has a class system called R6. If you want to use a java library with native rJava or jsr223 in R there is potentially a lot of glue code needed, and R library specific packaging configuration required. However if you don't mind writing an R-centric API in Java you can generate all of this glue code using a few java annotations and the normal javadoc annotations. This plugin aims to provide an annotation processor that writes that glue code and creates a fairly transparent connection between Java code and R code, with a minimum of hard work. The focus of this is streamlining the creation of R libraries by Java developers, rather than allowing access to arbitrary Java code from R. The ultimate aim of this plugin to allow java developers to provide simple APIs for their libraries, package their library using Maven, push it to github and for that to become seamlessly available as an R library, with a minimal amount of fuss. A focus is on trying to produce CI ready libraries tested with Github workflows and ready for CRAN submission.

HermiT is reasoner for ontologies written using the Web Ontology Language (OWL). Given an OWL file, HermiT can determine whether or not the ontology is consistent, identify subsumption relationships between classes, and much more. This is the maven build of HermiT and is designed for people who wish to use HermiT from within the OWL API. It is now versioned in the main HermiT version repository, although not officially supported by the HermiT developers. The version number of this package is a composite of the HermiT version and a value representing the OWLAPI release it is compatible with. Note that the group id for the upstream HermiT is com.hermit-reasoner, while this fork is released under net.sourceforge.owlapi. This fork exists to allow HermiT users to use newer OWLAPI versions than the ones supported by the original HermiT codebase. This package includes the Jautomata library (http://jautomata.sourceforge.net/), and builds with it directly. This library appears to be no longer under active development, and so a "fork" seems appropriate. No development is intended or anticipated on this code base.

This module defines the interface for AlignmentAlgorithms as well as some helper classes. An AlignmentAlgorithm computes an Alignment of two given input sequences, given a Comparator that works in these sequences. More details on the AlignmentAlgorithm can be found in the respective interface. More information on Comparators can be found in the comparators module. The resulting 'Alignment' may be just a real-valued dissimilarity between the input sequence or may incorporate additional information, such as a full Alignment, a PathList, a PathMap or a CooptimalModel. If those results support the calculation of a Gradient, they implement the DerivableAlignmentDistance interface. In more detail, the Alignment class represents the result of a backtracing scheme, listing all Operations that have been applied in one co-optimal Alignment. A classic AlignmentAlgorithm does not result in a differentiable dissimilarity, because the minimum function is not differentiable. Therefore, this package also contains utility functions for a soft approximation of the minimum function, namely Softmin. For faster (parallel) computation of many different alignments or gradients we also provide the ParallelProcessingEngine, the SquareParallelProcessingEngine and the ParallelGradientEngine.

This module defines the interfaces for Comparators in the TCS Alignment Toolbox. A Comparator has the purpose of defining the dissimilarity between elements in the input sequences of an Alignment. More specific information on Comparators can be found in the 'Comparator' interface. You can find a lot of helpful standard implementations of Comparators in the comparators-lib module. In the TCS Alignment Toolbox we require the output values of Comparators to lie in the range [0,1]. Many natural dissimilarities on value sets do not meet this criterion, such that additional normalization has to be applied. To that end this package also contains a Normalizer interface for functions that map real values from the range [0, infinity) to the range [0,1]. This package also provides a few convenience implementations of the Comparator interface to make the implementation of custom Comparators simpler, namely: SkipExtendedComparator, ParameterLessSkipExtendedComparator, ComparisonBasedSkipExtendedComparator, and ParameterLessComparisonBasedSkipExtendedComparator. Finally the TCS Alignment Toolbox also provides the means to learn parameters of Comparators. To enable that Comparators must implement the DerivableComparator interface to properly define the parameters that can be learned and the gradient of the dissimilarity with respect to these parameters. Gradients are stored using the Gradient interface as well as some convenience implementations of said interface, namely EmptyGradient, SingletonGradient, ArrayGradient and ListGradient.

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.