Download JAR files tagged by utilities with all dependencies

PowerMock allows you to unit test code normally regarded as untestable. For instance it is possible to mock static methods, remove static initializers, allow mocking without dependency injection and more. PowerMock works by bytecode manipulation. PowerMock also contain some utilities that gives you easier access to an objects internal state. PowerMock can be used to test otherwise untestable code and also to achieve a cleaner separation between test and production code.

A set of reflection utilities and miscellaneous utilities related to working with classes and their fields with no dependencies which is compatible with java 1.5 and generics. The package was originally created by Aaron Zeckoski for the Sakai Project and Generic DAO project but was repackaged to make it distributable by request. It is used in the RSF framework (http://www2.caret.cam.ac.uk/rsfwiki/) and the EntityBroker among other things. Note about the BeanUtils provided dependency: BeanUtils is not required if you are not using it in your project. Note about the Hibernate provided dependency: Hibernate is not required if you are not using it in your project.

The goal of quality-test is to provide a small Java library for basic code quality checks within unit tests. It is discouraged to use quality-test in production code. The checks provided in this library are designed to be used in unit-tests. The checks and utilities provided in this package check static properties of classes, for example whether classes are marked final or constructors are private. Additionally, there are utils to give additional code coverage for private constructors. Quality-Test often can be used together with Google Reflections (http://code.google.com/p/reflections/) to perform checks such as, assure that every class in package *.dto.* is final or make sure that no class in the package *.controller.* contains any non-final static variables.

What is jparsec? Jparsec is a recursive-desent parser combinator framework written for Java. It constructs parsers in native Java language only. Why yet another parser framework? Jparsec stands out for its combinator nature. It is no parser generator like YACC or ANTLR. No extra grammar file is required. Grammar is written in native Java /C# language, which also means you can utilize all the utilities in the Java/.Net community to get your parser fancy. What does "jparsec" stand for? Jparsec is an implementation of Haskell Parsec on the Java platform. Feature highlights. operator precendence grammar. accurate error location and customizable error message. rich set of pre-defined reusable combinator functions. declarative API that resembles BNF. [email protected] [email protected]

All Roads Lead to ROME. ROME is a set of Atom/RSS Java utilities that make it easy to work in Java with most syndication formats. Today it accepts all flavors of RSS (0.90, 0.91, 0.92, 0.93, 0.94, 1.0 and 2.0), Atom 0.3 and Atom 1.0 feeds. Rome includes a set of parsers and generators for the various flavors of feeds, as well as converters to convert from one format to another. The parsers can give you back Java objects that are either specific for the format you want to work with, or a generic normalized SyndFeed object that lets you work on with the data without bothering about the underlying format.

All Roads Lead to ROME. ROME is a set of Atom/RSS Java utilities that make it easy to work in Java with most syndication formats. Today it accepts all flavors of RSS (0.90, 0.91, 0.92, 0.93, 0.94, 1.0 and 2.0), Atom 0.3 and Atom 1.0 feeds. Rome includes a set of parsers and generators for the various flavors of feeds, as well as converters to convert from one format to another. The parsers can give you back Java objects that are either specific for the format you want to work with, or a generic normalized SyndFeed object that lets you work on with the data without bothering about the underlying format.

This project is a collection of miscellaneous tools shared by a number of projects of the same author. There are **simple tuples** to use with Java Streams, **type-safe maps** ([inspired by the heterogeneous map pattern](https://www.informit.com/articles/article.aspx?p=2861454&seqNum=8)) described in Effective Java by Joshua Bloch, a **finder** that handles in a smart way queries to data sources, a facility to use the **DCI (Data, Context and Interactions) architectural pattern**, a simple **message bus** suitable for using the pub-sub pattern inside an application, some **test utilities**, an **experimental actor framework** and a few other small things. Yes, the project name is a tribute to the [jazz standard with the same name](https://en.wikipedia.org/wiki/These_Foolish_Things_(Remind_Me_of_You)) by Maschwitz and Strachey.

Spring LDAP is a Java library for simplifying LDAP operations, based on the pattern of Spring's JdbcTemplate. The framework relieves the user of common chores, such as looking up and closing contexts, looping through results, encoding/decoding values and filters, and more. The LdapTemplate class encapsulates all the plumbing work involved in traditional LDAP programming, such as creating a DirContext, looping through NamingEnumerations, handling exceptions and cleaning up resources. This leaves the programmer to handle the important stuff - where to find data (DNs and Filters) and what do do with it (map to and from domain objects, bind, modify, unbind, etc.), in the same way that JdbcTemplate relieves the programmer of all but the actual SQL and how the data maps to the domain model. In addition to this, Spring LDAP provides transaction support, a pooling library, exception translation from NamingExceptions to a mirrored unchecked Exception hierarchy, as well as several utilities for working with filters, LDAP paths and Attributes.

GroupDocs.Metadata for Java is a class library to process metadata associated with various Document, Image, CAD, Audio, Video and Archive formats. Key Features: - Covers most popular metadata standards: XMP, EXIF, IPTC, Image Resource Blocks, ID3, document properties - Covers most popular document formats: Microsoft Word, Microsoft Excel, Microsoft PowerPoint, PDF, Microsoft OneNote, Microsoft Visio, Open Document Format - Covers most popular image formats: BMP, GIF, DjVu, JPEG, PNG, TIFF, PSD, WebP, WMF, EMF, DICOM - Covers most popular email formats: Outlook Message, Email Message - Covers most popular audio formats: Mp3, WAV - Covers most popular video formats: Avi, Mov - Create, modify and remove metadata associated with supported document and image formats with a few lines of code - Manage EXIF metadata in Jpeg and TIFF formats - Manage XMP metadata in image and PDF formats - Manage Image Resource blocks in image formats - Manage audio metadata: ID3 tag (ID3v1, ID3v2), Lyrics3 tag, APE - Utilities to inspect and clean hidden metadata in document formats - Utilities to Search and Compare all metadata - Utilities to Export metadata to Excel/Csv - Metadata cleaner utility - MIME type detection - Read track changes. Accept or reject track changes - Read EXIF maker-notes: Sony, Nikon, Canon, Panasonic For more details on the library, please visit GroupDocs website at: https://products.groupdocs.com/metadata/Java Note: The library comes up with some limitations in the evaluation mode. In order to test full features of GroupDocs.Metadata for Java library, please request a free 30-day temporary license.

This is an implementation of the Neural Gas algorithm on distance data (Relational Neural Gas) for unsupervised clustering. We recommend that you use the functions provided by the RelationalNeuralGas class for your purposes. All other classes and functions are utilities which are used by this central class. In particular, you can use RelationalNeuralGas.train() to obtain a RNGModel (i.e. a clustering of your data), and subsequently you can use RelationalNeuralGas.getAssignments() to obtain the resulting cluster assignments, and RelationalNeuralGas.classify() to cluster new points which are not part of the training data set. The underlying scientific work is summarized nicely in the dissertation "Topographic Mapping of Dissimilarity Datasets" by Alexander Hasenfuss (2009). The basic properties of an Relational Neural Gas algorithm are the following: 1.) It is relational: The data is represented only in terms of a pairwise distance matrix. 2.) It is a clustering method: The algorithm provides a clustering model, that is: After calculation, each data point should be assigned to a cluster (for this package here we only consider hard clustering, that is: each data point is assigned to exactly one cluster). 3.) It is a vector quantization method: Each cluster corresponds to a prototype, which is in the center of the cluster and data points are assigned to the cluster if and only if they are closest to this particular prototype. 4.) It is rank-based: The updates of the prototypes depend only on the distance ranking, not on the absolute value of the distances.