Parameter Catalogs for Simulation

A good part of computer science was and is driven by the motivation to make it easier to develop computer programs of all sorts. "Higher" programming languages were invented to make programs human readable and soon special constructs for functional programming (computation without side effects) and structured programming (computation without go to statements) were introduced to help programmers writing and understanding ever growing programs. Then, between 1962 and 1967, program language Simula was developed especially to deal with the challenges of simulating systems comprising of many different types of objects. This opened the door to more direct computer representations of real world objects, their attributes, relationships and behavior, ultimately leading to object-oriented software development that today is embodied in programming languages like Java, C++, Python, and graphical notations like the Unified Modeling Language (UML).

While these achievements had boosted the productivity of software developers, still the creation of correct, efficient and maintainable programs — including simulations — required a big deal of expert knowledge and experience. To overcome this bottleneck, starting in the 70s, so called 4th generation languages entered the stage. These languages were tailored to specific tasks like statistics ("S" 1976, "R" being its successor), database programming (SQL 1979), or simulation (MATLAB around 1979, Mathematica 1988, Modellica 1999) to name a few. By sacrificing generality, these special languages become more accessible to domain experts, not just trained software developers. To flatten the learning curve even more, formal graphical languages for special purposes were invented, e.g. Simulink for block diagram simulation models in 1984, Entity-Relationship-Diagrams for data modeling in 1976, UML for object-oriented systems design in the 1990s, or graphical languages to specify business and also scientific workflows around 2000.

This very short history of technologies for development of software in general, and simulations in particular, shall illuminate the tools at our disposal:

Now back to the task at hand.

Some domains deal with a few types of simple objects to be simulated. Take the building blocks of an electric circuit as an example. The algorithms to simulate these correctly and efficiently may be quite complex — the model elements usually can be described by very few parameters like resistance or capacity. More complex domains like (regenerative) energy systems or building physics deal with more complex objects to be simulated, e.g. PV modules or layered walls of buildings, often coming in different types and configurations, and dozens of possibly interdependent parameters.

First a note on terminology: Instead of parameter catalogs in SimStadt we used term library like in building physics library. Obviously this was not a good choice, since library is used a lot in IT and programming with all sorts of meaning. Instead we started to talk about data catalogs, but in data science this term has specific meaning, namely: catalogs of data and data sources. Since our catalogs, first of all, shall grant structured access to parameters for simulated entities parameter catalog sounds more appropriate to me.

The problem of navigating huge parameter spaces and assembling complex simulation models popped up as the author worked on a diagram editor for INSEL, a simulation language and runtime environment developed for renewable energy systems simulation. To make existing catalogs on weather data, solar panels and inverter modules accessible to the modeler, special dialogs were added to the INSEL user interface that allowed browsing through the catalogs. Using this browsers, the modeler would choose a weather station, panel or inverter to parameterize a corresponding INSEL function-block. However, there are some severe disadvantages with this approach:

From 2013 to 2016, the simulation platform SimStadt was developed to make specific modeling and simulation workflows accessible to experts in urban planning and energy systems. Using INSEL and other simulators under the hood, the usage of 3D city data, provided as CityGML files, was a core requirement of this project.

To enable simulation of, say, the heating demand of a district, geometric building data had to be enriched with data on building physics and usage. To do so, existing informations about building physics and usage — often only available as informal typologies or tables — had to be provided to the SimStadt user on an abstract level, e.g. to choose between refurbishment scenarios. At the same time, specific building configurations and parameter sets had to be injected into the simulation models to obtain the desired results.

Again, we implemented parameter catalogs to fulfill these requirements, but compared to the quite simple catalogs used in INSEL, the data for building materials, window, wall and roof types as well as the typologies of buildings, households, usage patterns, and so on were more intricate. They had to be created iteratively in collaboration with domain experts. In this situation, manual coding data formats and access with a general programming language would have led to relatively long iteration cycles and high communication effort between programmer and domain expert. Instead, we decided to use a DSL for data modeling and use code generation whenever possible. Since SimStadt was developed within the Java eco-system we followed this standard approach:^[1]

After the data model for building physics catalogs had matured, we developed a desktop application for convenient creation and maintenance of building physics data catalogs separate from SimStadt. It was developed in Java with a user interface written in JavaFX and was well received by domain experts.

However, as a different catalog — this time for building usages — had to be created, it was quite difficult to reuse the XML schema and application code from the building physics catalog: The usage catalog data model was "pressed" into a form similar to the building physics catalog data model, and the UI code was "over-engineered" to accommodate both catalog’s requirements.

From INSEL and SimStadt we learned, that manual and automatic construction and parameterization of complex simulation models with many types of interrelated objects should be supported be the means of domain specific parameter catalogs.

Close collaboration with domain experts in designing and implementing these catalogs in short development cycles is desirable.

Parameter catalogs and the software for their creation, maintenance and deployment should be independent of any specific simulation software, (a) to be reusable and (b) not to overload simulation applications.

In SimStadt, catalog development was partly facilitated by a textual DSL for data modeling (XML schema language) and automatic generation of Java code from it. On the other hand, user interfaces and generation and parameterization of simulations from templates within SimStadt workflows had still to be coded manually hindering the routinely creation of new catalogs.

Now, in 2020, several developments in different projects provide an opportunity to re-think the topic of parameter catalogs for simulations, namely:

Plans are to use the same approach also for implementation of (3). Since task (2) and maybe (1) will use Eclipse, too, close integration of parameter catalogs and simulation environments seems feasible. E.g., a user could drag an electric system component from a catalog onto an INSEL block for parametrization.

The Eclipse application framework offers:

As always, all that glitters is not gold. When we go through the details below, some bugs and inconsistencies, typical for open source projects of this age and size, have to be addressed.

Eclipse was originally developed by IBM and became Open Source in 2001. It is best known for its Integrated Development Environments (Eclipse IDEs), not only for Java, but also for C++, Python and many other programming languages. These IDEs are created on top of the Eclipse Rich Client Platform (Eclipse RCP), an application framework and plug-in system based on Java and OSGi. Eclipse RCP is foundation of a plethora of general-purpose applications, too.

First time users of Eclipse better understand the following concepts.

An Eclipse package is an Eclipse distribution dedicated to a specific type of task.^[3] A list of packages is available at eclipse.org. Beside others it contains Eclipse IDE for Java Developers, Eclipse IDE for Scientific Computing, and Eclipse Modeling Tools. Note that third parties offer many other packages, e.g. GAMA for multi-agent-simulation.

An installed Eclipse package consists of a runtime core and a bunch of additional plug-ins. Technically, a plug-in is just a special kind of Java archive (JAR file) that uses and can be used by other plug-ins with regard to OSGi specifications. Groups of plug-ins that belong together are called a feature.

Sometimes, a user will add plug-ins or features to an Eclipse installation to add new capabilities. E.g. writing this documentation within my Eclipse IDE is facilitated by the plug-in Asciidoctor Editor. Plug-ins can easily be installed via main menu command Help → Eclipse Marketplace…​ or Help → Install New Software…​. Some plug-ins may be self-made like our City Units plug-in that enables Ecore to deal with physical quantities.

Git is the industry standard for collaborative work on, and versioning of, source code and other textual data. Collaborative development of parameter catalogs benefits massively from using Git. Git support is built into Eclipse Modeling Tools, the Eclipse package we will use. However, if Eclipse needs to connect to a Git server that uses SSH protocol (not HTTPS with credentials), access configuration is more involved and may be dependent on your operating system.

Some users, anyway, prefer to use Git from the command line or with one of the client application listed here, e.g. TortoiseGit for Windows.

While it is required to get Git working at some point, we won’t refer to it in this document and, for now, do not cover the installation of Git on your machine or configuration of Git in Eclipse.

When you start a new Eclipse installation for the first time, you are asked to designate a new directory in your file system to store an Eclipse workspace. Eclipse is always running with exact one workspace open. As the name implies, a workspace stores everything needed in a given context of work, namely a set of related projects the user is working on as well as meta-data like preference settings, the current status of projects, to do lists, and more. In case a user wants to work in different contexts, e.g. on different tasks, command File → Switch Workspace allows to create additional workspaces and to switch between them.

An Eclipse project is a technical term for a directory that often contains:

File → New → Project…​ offers many different types of projects that the user can choose from, e.g. Java projects to create Java programs, Ecore modeling projects, or general projects, that simple hold some arbitrary files.^[4]

The projects belonging to a workspace can either be directly stored within the workspace as sub-directories (the default offered to the user when creating a new project), or linked from it, that is the workspace just holds a link to the project directory that lives somewhere in the file system outside of the workspace. Linking allows to work with the same projects in different workspaces.

While it sometimes makes sense to share or exchange workspaces between users,^[5], I do not recommend this for now. Projects, in contrast, are shared between users most of the time, usually via Git. In general, I would suggest to store Eclipse projects outside workspaces at dedicated locations in the user’s file system. That way, we can follow the convention that local Git repositories should all be located under <userhome>/git.

Starting with version 2022-03 Eclipse packages come with a bundled Java Development Kit (JDK), version 17. Thus, there is no need anymore to install a JDK manually.

Our graphical and form based modeling tools, e.g. Insel 9.0 and Parameter Catalogs, run on top of Eclipse Sirius, an open source framework that provides a set of features and plugins that can be added to any Eclipse package to transform it into a very flexible modeling workbench.

We will install Eclipse package Eclipse Modeling Tools first and, then, Eclipse Sirius on top. To get current version 4.23 (2022‑03) of Eclipse Modeling Tools, go to www.eclipse.org/downloads/packages/. On this page you may see "Try the Eclipse Installer" or similar. Do not follow this advice, since we want more control over what will be installed. Instead, look for package Eclipse Modeling Tools and click the download link for your operating system and system architecture displayed on the right:

Finally, click on Download and wait for the 550 something MB package to arrive. On macOS this is a disk image file (.dmg) that is mounted by a double click. Drag and drop Eclipse to folder Applications and rename the copy to something more specific like EclipseModeling2203.

On windows and Linux unzip/extract the downloaded file and possibly rename the resulting application to EclipseModeling2203 or similar. Then, move the application to a suited directory, but not Programs or Programs (x86) on Windows!

After installation has finished, launch the application for the first time and you will see a dialog for choosing a new empty directory as its workspace.

More workspaces might come into existence later, so replace the proposed generic directory path and name with a more specific one, e.g.EclipseWSCatalogs. The main window appears with a Welcome Screen open. Especially under Workbench Basics you will find exhaustive documentation on Eclipse that might be of interest later, e.g.:

For now, you can dismiss the welcome screen. It can be opened anytime by executing Help → Welcome.

Now you should see the initial window layout with Model Explorer and Outline on the left and a big empty editing area to the right with a Properties view below.

While package Eclipse Modeling Tools already contains Sirius features that let you create graphical Ecore models, features for specifying your own forms and graphical editors are still missing. You can add these features easily with Eclipse’s built-in update mechanism:

Now we use the same update mechanism to add software components that were created specifically for parameter catalogs and hosted at the transfer portal of HfT Stuttgart.

Parameter catalogs should be able to represent quantities, not just bare numbers. See Unit of measurement libraries, their popularity and suitability for a systematic account of open source solutions in the this area. Java provides an extensive framework to deal with quantities and their units defined in Java Specification Request (JSR) 385. The reference implementation for this framework is Indriya. Demos of its usage can be found at https://unitsofmeasurement.github.io/uom-demos/.

While Indriya offers all common SI units and more, the author provided additional units specific to urban simulation plus specialized Ecore types for specifying these units for attributes in Ecore data models as an Eclipse feature named City Units.

The new types are named Quantity and TimeOfDay. Some examples of valid unit symbols are listed in this README.

To install City Units, open dialog Help → Install New Software…​ and enter site https://transfer.hft-stuttgart.de/pages/neqmodplus/de.hft-stuttgart.cityunits/p2repo like depicted below.

Select City Units, press Next > and acknowledge all following dialogs, including security warnings.

Finally, restart Eclipse to complete installation.

Before we start working on real catalog projects hosted in a Git repository in the next section, let us first create a demo project for playing around and learning basic modeling skills.

It takes time and effort to come along with good names for model entities, projects, files, and so on. Also, specific naming conventions are in place to enhance readability of models and program code. Since it is not always clear where names provided during modeling are used later, I compiled a list of names important in Ecore projects and added examples and comments to elucidate their meaning and naming conventions.

Classes are written in Camel case notation starting with an upper case letter. Associations and attributes are written the same way, but starting with a lower case letter.

All other names should be derived from the globally unique name space of the project, in our example: example.org/democatalog. It consists of a global unique domain name and a path to the project, unique within that domain.

Use the names of example Demo Catalog to create your first Ecore modeling project:

Eclipse should look like below with an new empty graphical Ecore diagram editor opened. The diagram is automatically named democatalog after the package name for the Java classes that will be generated from it (provided above). The Model Explorer shows the contents of the new Ecore modeling project.

To get your feet wet, do this:

Technically, everything is in place now to begin modeling the data that the projected catalog shall contain. Except …​ understanding the basics of object-oriented modeling would be helpful. This is why developers should support domain experts at this stage.

Ecore diagrams are simplified UML class diagrams. Here some resources on what this is all about:

We will touch central object-oriented concepts Class, Object, Attribute, Association, Composition, and Multiplicity in an example below, but work through above sources to get a deeper understanding and to enhance your modeling skills.

Note that above sources differentiate between conceptual and detailed models. We go for detailed models, since only these contain enough information to generate code. Having said this, it is usually a good idea to have two or three conceptual iterations at a white board to agree on the broad approach before going too much into detail. But even if one starts with Ecore models right away, these also can be adapted any time to follow a new train of thought.

See here the essential and typical structure of a parameter catalog in a class diagram. Instead of artificial example classes like Foo and Bar it shows classes from an existing catalog, albeit in very condensed form.

The diagram models four types of technical components whose data shall be stored in the catalog, e.g. for parameterization of simulation models later: Boiler, CombinedHeatPower, SolarPanel, and Inverter.

The catalog itself is represented by class EnergyComponentsCatalog. Unlike dozens, hundreds, or even thousands of objects to be cataloged — Boilers, Inverters etc. — there will be just exactly one catalog object in the data representing the catalog itself. Its "singularity" is not visible in the class diagram, but an Ecore convention requires that all objects must form a composition hierarchy with only one root object.

If, in the domain, one object is composed of others, this is expressed by a special kind of association called composition. Compositions are depicted as a link with a diamond shape attached to the containing object. In the Boiler case said link translates to: The EnergyComponentsCatalog contains — or is composed of — zero or more (0..*) boiler objects stored in a list named boilers.

Besides composition of objects, the model above shows another, completely different, kind of hierarchy: the inheritance hierarchy between classes. Whenever classes of objects share the same attributes or associations, we don’t like to repeat ourselves by adding that attribute or relation to all classes again and again. Instead, we add a super class to define common attributes and associations and connect it to sub classes that will automatically inherit all the features of their super class.

In our example above, common to all four energy components are attributes modelName and revisionYear, thus these are modeled by class EnergyComponent that is directly or indirectly a super class of Boiler, CombinedHeatPower, SolarPanel, and Inverter. Similar, Boiler and CombinedHeatPower share attribute installedThermalPower factored out by class ChemicalDevice. SolarPanel and Inverter share attribute nominalPower modeled in abstract class ElectricalDevice.

You probably noticed a fifth type of objects contained in the catalog, namely Manufacturer objects stored in list manufactureres. How come? Ok, here is the story:

Observe in our data model, reference producedBy points from EnergyComponent to Manufacturer making it uni-directional reference. One can simply query the manufacturer of a product, but not the other way around. With a bi-directional reference both queries would be available.

Observe also the annotations 0..* and 1..1 near class Manufacturer. These are multiplicities of associations: An EnergyComponentsCatalog contains zero, one, or many objects of class Manufacturer and an EnergyComponent must reference exactly one manufacturer — not less, not more.

Obviously, attributes are central in data modeling. Create one by dragging it from the palette onto our one and only class so far: EnergyComponentsCatalog. The class symbol will turn red to indicate an error. Hover with the mouse pointer over the new attribute and a tooltip with a more or less helpful error message will appear. Current error is caused by that no data type was set for the new attribute. Data types for attributes can be integer or floating point numbers, strings, dates, booleans, and more. To get rid of the error:

There exists one other means to define the values an attribute can take, namely enumerations of distinct literals. Take Monday, Tuesday, Wednesday, …​ as a typical example for representing weekdays. In our example data model you’ll find one Enumeration named BoilerType with values LowTemperature and Condensing.

The next section deals with generation of Java code from data models. To have more to play with, please implement our example model in Ecore now.

Two more tips and you are ready to rock and roll! — At least with your homework.

That’s it for the data modeling part. By now, your Ecore model should look like this:

In this section you will get a glimpse on how to create an application to create and edit data conforming to the Ecore data model of our demo parameters catalog.

Topics described here (and much more) are discussed in this Sirius Starter Tutorial.

If you are less interested in the details of UI creation, but more in working on already existing parameter catalog software and data, you may skip this section for now and proceed with [Working with Git Hosted Catalogs].

Let us bring the Ecore data model to life, that is, generate code from it that allows to create, read, update, and delete (CRUD) concrete data objects of modeled classes in computers:

Firstly, launch a new instance of the running Eclipse application:

Secondly, create a project that will contain catalog data (remember Eclipse can only handle files that are part of a project):

Thirdly, create a first XML file for catalog data:

A new entry named First.democatalog should appear in the Model Explorer. Double-click it and a generic editor will open. In principle, one could use this editor to add new data to the catalog via New Child > in the context menu over entry Energy Component Catalog. Data of a selected entry can be edited in view Properties that is generic, too.

Please add two or three boilers this way to have some data to play with below.

When done, you may save First.democatalog so that after closing the application data will reappear if it is opened again as described above.

The generic editors don’t get as far. Usually, one would like to have tables, custom property sheets, input validation, and more. Well, Sirius is all about creating nice graphical and form-based editors for data models specified in Ecore. To do this we need one more Eclipse project.

Like the data of our catalog is modeled as an Ecore file using a dedicated graphical editor, so will the user interface (tables, trees, diagrams, property views) be modeled in a Sirius .odesign file that lives in a special Eclipse project that we create while still in the (second) Eclipse application that hosts the data:

A special editor for file democatalog.odesign appears automatically. In it select MyViewpoint and rename it in Properties to Catalog. A viewpoint provides a set of representations (tables, trees, diagrams, property views) that end-users can instantiate.

In what follows, we work with the democatalog.odesign editor. Say, we want to add a table for boilers to the UI:

From the above Boiler_tables know that they present data conforming to our http://example.org/democatalog data model and that boiler data are found as part — or "below" — the Energy Component Catalog.

Next, specify the lines to be displayed in the table:

And now for the columns:

After addition of some foreground and background styles, the design of the UI looks like this.

Save it!

To create an instance of the table just designed double-click on representations.aird in the data project:

In case viewpoint Catalogs under header Representations is still disabled as shown above, select it and press Enable. Then:

Note, that you can delete boilers from the table’s context menu, but currently there is no button or menu entry to create new boilers. Such a command would have to be described in democatalog.odesign first.

Be aware that applications with UI design and example data launched from development environment are meant to be prototypes for the final software only. In fact, any saved changes in the design file are instantly reflected in the UI. During refinement of model and UI, data sets can be created, edited, and tested for usability without the need to built deployable software component. (On deployment, see parts Accessing and Using Parameter Catalogs and Build (Parameter Catalog) Applications with Eclipse Tycho below.)

Iteratively the UI design must be adapted to changes in data model, although some changes are automatically reflected in the generated UI, at least for default forms. Data model changes can also can render existing XML data incompatible. There are tools for data migration, but for now, recreation of test data or manual editing of XML file is the way to go.

As you may imagine, this is just the tip of the iceberg of what can be done with the Sirius framework for designing graphical UIs. While domain experts should be capable to create and to refine Ecore data models, the UI design of a parameter catalogs will mainly be done by software developers. However, since the UI is not implemented by program code, but a description in an .odesign file, domain experts can easily enhance and tweak it, e.g. by adding or reordering columns of a table.

Ecore data models and Sirius based UI design are used to create parameter catalog software hosted in Git repositories. To work with these, all you need is Java, Eclipse with required plug-ins for Sirius and Unit handling (see Setup Eclipse Modeling Tools for details.)

To connect to a Git repository open the Import Projects from Git wizard via File → Import…​ → Git → Projects from Git → Clone URI. Then:

Now you can work on the data model like you did with the demo catalog. Find it under model in ca.concordia.usp.greenerycatalog.model (compare fig. New Ecore Modeling Project).

For data inspection and editing — and possibly modifying the UI — launch a new instance of the running Obeo Designer application by executing Run → Run Configurations…​, double-click on Eclipse Application to get a New_configuration and give it a meaningful name (e.g. GreeneryCatalog). Then, press Run to start the application, close the welcome screen and open the Sirius perspective using the suited button in the top right corner of the main window.

Now, import the projects that contain data and UI design, respectively:

When closing the application, it asks to store or dismiss any changes in data or UI design. You can also save these any time with File → Save All.

For simplicity, the demo catalog only used built-in attribute types like EDouble, EInt, or EString. On the other hand, real-world parameter catalogs use a custom type named Quantity that combines a numerical (double) value with a unit.

Symbols for defining units follow SI and other standards, including decimal prefixes like m for Milli or G for Giga as well as derived units, that is: mV, GV or kW·h/m³ are all valid unit definitions. This is all documented well in the resources mentioned in section Add Plug-ins to deal with Quantities and Units above, but for convenience, a table with valid units, including some specific units for urban simulation, is compiled in UnitsExamples.md.

To set an attribute’s type to Quantity just select it in the model, choose tab Semantic in view Properties, click on EType and select Quantity from the list of available types. In the figure below, this was already done.

The red arrow shows how a unit is defined in field Default Value Literal. E.g., attribute densityOfDrySoil has unit kg/m³ assigned to it.

Note that, for this attribute, no numerical default value is given. In contrast, conductivityOfDrySoil is given a unit and a default numerical value: 1.0 W/(m*K).

The rules for how a Quantity default value is converted to its unit and default (initial) numerical value are very "forgiving":

By this rules, any string — including the empty string — will be interpreted as a Quantity somehow.

What is the point in declaring a dimensionless quantity for an attribute, anyway, instead of just declare it EDouble or EInt? The answer is that quantities can — and most of the time will — have a range of valid values defined.

In this editor, define a range like this:

If a catalog’s end user tries to enter a number outside the given range in the UI, it will be adapted automatically to a valid value.

Again, domain experts use a specific annotation to provide short help texts that inform end-users about an attribute’s purpose, range and so on. (These texts are displayed as tooltips when the users mouse stays on top of a question mark.) And again, this is possible only in another kind of editor — this time the editor that is opened on a .genmodel in Model Explorer. Each Ecore model is accompanied by a .genmodel that lives besides the respective .ecore file. Open the required editor from its context menu with Open With → EMF Generator.

The picture below shows the details. Just open the .genmodel tree until you can select the attribute that shall be documented.

In its Properties provide the tooltip in field Edit → Property Description. In this example, the same text was also copied to Model → Documentation. These texts are automatically inserted into comments in the generated program code, so that they can inform a programmer that wants to use the generated API.

Congratulations on making it this far. What have we achieved?

We get to know Eclipse Modeling Tools and created a graphical Ecore data model with one catalog class and five classes/types of domain objects therein. Classes have been defined by name, attributes, and relationships between them, often with cardinalities. Whenever classes shared some attributes or relationships we factored these out into super classes. An enumeration introduced a new attribute type as a set of named values.

From this data model, we issued commands to create Java code for representing the data in memory as well as to store and retrieve them on and from disk. Methods to create, read, update and delete data objects (CRUD) were generated, too. We implemented a prototypical user interface for this data with Eclipse Sirius by providing a .odesign model for that very UI.

Lastly, we started working on real world parameter catalogs hosted in git repositories and introduced Quantity as a custom attribute type to model quantities as numerical values with defined units.

To access catalog data via Python, we use PyEcore, a Python re-implementation of Ecore (see PyEcore Documentation)

While it is possible to work with an Ecore data model and according data in a purely dynamical fashion by accessing data just by name using reflection, here we prefer a "statical" approach, that generates explicit Python classes from the Ecore data model in a first step according to this part of the above documentation.

After installation of the required Python libraries pyecore and pyecoregen (see documentation above), this command generates Python classes from an Ecore data model:

Here we assume an Ecore data model EnergyComponentsForPython.ecore to be located in the current directory and the output be generated in a new directory located besides the model file. See contents of python in project /de.hftstuttgart.energycomponents.model as an example.

Note that EnergyComponentsForPython.ecore is different from the original EnergyComponents.ecore in that attribute type Quantity was replaced by String for now, since a suited Python library to represent Units and Quantites has not been choosen yet.

With Python Ecore libraries and generated classes in place, according catalog data can be imported and queried like so:

With HfTStuttgart.energycomponents as input, we get:

We are going to create a complete Eclipse desktop application from generated code. We also want to deploy this application for Linux, macOS and Windows operating systems. Eclipse offers several approaches for compiling and deploying such an application, traditionally with Ant scripts.

Creation and maintenance of these scripts turned out to be tedious and error prone. For quite some years now, the proposed — and mostly supported — method for building Eclipse applications is to use Maven build system, more specifically, a couple of Maven plug-ins, subsumed under the name Tycho.

Many Eclipse platforms have Maven support M2Eclipse already built in, not so our Eclipse Modeling Tools. But don’t worry: Installation of required Eclipse feature is easy and straight forward. And, by the way, you will acquire the indispensable skill of how to install new plug-ins/features to Eclipse.

First, tell your Eclipse installation where to look for the new software. Execute Help → Install new Software…​ to invoke dialog Available Software and press Add…​. Sub-dialog Add Repository pops up.

In there provide m2e as name and

as location. After confirmation with Add, choose the new site to Work with: Eclipse now looks up the site for available software.

Provided Group items by category is checked, above features are displayed. Check all features and confirm all following questions about licenses and security concerns. After download is complete — it can take a few minutes — restart Eclipse. Verify that Maven version 3.6.3 or above is now installed in Window → Preferences…​ (or Eclipse → Preferences…​ on macOS) under Maven → Installations.

"Correct" way to add third party Java libraries as plugins

Example Indriya