Download JAR files tagged by combination with all dependencies

Maven plugin for small application config support. Allows you to define configuration for your application at one place and generate config files and java class. Configuration can be instantiate by var args, file config or environment variables or combination of all. ISSUES: -v potomkovi mam rucne psanou pomocnou property, ta se nesmi dostat do property descriptoru, pak je tam s klicem null -pokud reloadArgs byposition vrati null, vyhodit hned vyjimku - confDef = byOrder.get(iPos++); TODO: - boolean opts -- is/get -- option without value true - make def enum public - static instance to access from everywhere without pass an instance -static enum Def -more executions with different configuration has conflict in options and names

This powerful reporting library aims to simplify producing reports using iText. Looking at producing reports with iText the most demanding tasks are how to get from data to report blocks, how to style those blocks and how to evaluate (debug) the resulting report. Exactly those three major tasks are well supported by the VectorPrint report library: - (combination of) xml configuration and annotations to transform existing Java data objects into report block - provide styling information (css like) seperate from code - a firebug like debug mode to help evaluate your report.

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.

This module contains a more general approach to construct AlignmentAlgorithms by relying on the theoretical concept of Algebraic Dynamic Programming (ADP) as developed by Giegerich et al. ADP defines four ingredients for an alignment algorithm: 1.) A signature that defines the permitted alignment operations. Operations are just function templates with an associated arity, meaning the number of arguments it takes from the left sequence and from the right sequence. In the TCSAlignmentToolbox we have a fixed signature with the following operations: REPLACEMENT(1, 1), DELETION(1, 0), INSERTION(0, 1), SKIPDELETION(1, 0) and SKIPINSERTION(0, 1) 2.) A regular tree grammar that produces alignments, that is: sequences of operations, in a restricted fashion. 3.) An algebra that can translate such trees to a cost. In the TCSAlignmentToolbox this is a Comparator. 4.) A choice function, in case of the TCSAlignmentToolbox: the strict minimum or the soft minimum. An alignment algorithm in the TCSAlignmentToolbox sense of the word then is the combination of choice function and grammar. While we provide hardcoded versions of these combinations in the main package, the adp package allows you to create your own grammars. You can combine them with a choice function by instantiating one of the Algorithm classes provided in this package with a grammar of your choice. For example: AlignmentAlgorithm algo = new SoftADPScoreAlgorithm(my_grammar, comparator); creates an alignment algorithm that implicitly produces all possible alignments your grammar can construct with the given input, translates them to a cost using the algebra/comparator you provided and applies the soft minimum to return the score. This all gets efficient by dynamic programming. Note that there is runtime overhead when using this method in comparison with the hardcoded algorithms. But for complicated grammars this is a much easier way to go. For more information on the theory, please refer to my master's thesis: "Adaptive Affine Sequence Alignment using Algebraic Dynamic Programming"