Xtext 2.14 released!

The Xtext team proudly announces the availability of the 2.14 release. While the previous releases have focused on performance and internal improvements this release comes with a bunch of new features that will ease your life and make your DSLs even more valuable. We encourage to update to this release. Xtext 2.14 will also be part of the upcoming Eclipse Photon release scheduled for June 27. Read more >

New File Wizard – easy to generate with Xtext 2.14

We have already shown on our blog how to generate a new project wizard with Xtext 2.14. Now it's also possible to create a comparable wizard, also based on templates, for creating DSL files in existing projects. Read more >

Generating a new project wizard with Xtext 2.14

Anyone who implements DSLs with Eclipse Xtext benefits from the fact that the necessary infrastructure is generated automatically from the grammar. This gives you an environment in which everything is provided for developing your own DSL with minimum effort. A mere grammar and two buttons later, you can start Eclipse, full of anticipation. It first asks what kind of project you want to create. Xtext has not answered this question yet, so you have a choice. Read more >

Code Mining Support in Xtext

One of the most notable new APIs in Eclipse Photon is called Code Mining. A code mining represents content (i.e. labels and icons) that are shown within the text editor, but are not part of the text itself. For example, within a method call statement a mining could display the name of parameters, or an icon above unit test methods could be shown that runs the test on clicking it. Read more >

What is Design Thinking?

Design Thinking is one of the much-hyped topics that we encounter again and again. But what is behind it? What does "thinking creatively" mean? What do I need to be able to achieve it? Do I have to be a designer? I will try to get to the bottom of such questions, and in the process provide an introduction to the topic. Read more >

Internship at itemis: Robocar showcase of machine learning

At itemis, we are involved in automotive software projects in terms of modeling (domain specific languages for architecture and behavior), tooling (architecture, feature models, implementation, Machine Learning) and concepts/standards (AUTOSAR, Genivi, openADX). At our office in Stuttgart, we wanted to set up a tangible demonstrator – a robocar platform as a flexible base with an initial showcase of machine learning. Read more >

The Business DSL: Zurich Insurance

Insurance products are complicated. They involve sophisticated math and lots of interacting rules. They exhibit significant variability between different markets. They change over time, for example, driven by changes in law or updated risk assessments from the company. In addition, once consumers sign an insurance contract, they must not be affected by changes to that product (or at least they must not be worse off), which means that “old” contracts must continue to be executed with the “old” logic. Read more >

YAKINDU Statechart Tools March release – new and noteworthy

We released YAKINDU Statechart Tools Standard and Professional Edition version 3.3.0 today!  In the last three months, our team closed 126 issues in total. Here are the new and noteworthy changes in the new version: Read more >

How Visualizing Traceability Data Removes the Worries of Project Management – Part 2

A previous post outlined an example project and a set of key metrics (KPIs) that I want to be able to calculate based on information gathered from multiple different development tools. In this follow up post, I will illustrate how to calculate one of those metrics using traceability information and an example query over the trace graph. Read more >

Building Domain-specific Languages with Xtext and Xtend

Specifying the requirements of a software system and converting such a specification into executable source code is difficult and error-prone. Requirements specifications written in prose are often ambiguous and hard to understand for developers. Therefore, the process of turning this documents into software is slow and prone to error. Domain-specific languages (DSL) challenge this problem by defining a semantically rich notation to describe domain concepts clear and concise. Read more >


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