The TTC 2015 Train Benchmark Case

Note. This repository was used for the 2015 Transformation Tool Contest and is no longer maintained. If you wish to use the benchmark, visit the Train Benchmark repository, which is actively maintained.

Case description

The paper/trainbenchmark-ttc.pdf file contains the case description.

Prerequisites

• 64-bit operating system (Ubuntu-based Linux systems are recommended)
• Oracle JDK 7+
• Maven 3.0+

Platform dependencies

1. Install Maven 3 and make sure it is on your path (check with mvn --version).
2. Make sure you have Python 3 installed.

Using the framework

Use the scripts/init-config.sh file to initialize the config/config.json configuration file.

The scripts directory contains the run.py script which is used for the following purposes:

• run.py -b -- builds the projects
• run.py -b -s -- builds the projects without testing
• run.py -g -- generates the instance models
• run.py -m -- runs the benchmark
• run.py -v -- visualizes the results of the latest benchmark

The config directory contains the configuration for the scripts:

• config.json -- configuration for the model generation and the benchmark (based on the config-default.json file)
• reporting.json -- configuration for the visualization

Generating instance models

Set the maxSize variable to the desired value and run the run.py -g script. With enough memory (-Xmx2G or more), the models from size 1 to size 512 are generated in about 5 minutes.

Running the benchmark

The script runs the benchmark for the given number of runs, for the specified tools, queries and sizes.

The benchmark results are stored in a TSV (tab-separated values) file. The header for the TSV file is stored in the output/header.tsv file.

Reporting and visualization

Make sure you read the README.md file in the reporting directory and install all the requirements for R.

Importing to Eclipse

It is recommended to start with an Eclipse distribution tailored to developing EMF-based applications, e.g. Eclipse Modeling.

If you'd like to try the ATL/EMFTVM implementation, it is recommended to use Eclipse Luna. There are two ways to resolve the dependencies:

1. Maven dependencies (pom.xml files). This requires the m2e Eclipse plugin (this is included in Eclipse for Java developers but is not included in Modeling distribution). The m2e plugin can be installed from the the update site of your release (Kepler/Luna).
2. Plug-in dependencies (MANIFEST.MF files).
   • Use the Orbit update site for your release (http://download.eclipse.org/tools/orbit/downloads/) to install the Apache Commons CLI and the Guava: Google Core Libraries for Java Source plug-ins.
   • To use the ATL/EMFTVM implementation, install the ATL/EMFTVM and SimpleGT plug-ins from http://marketplace.eclipse.org/content/atlemftvm and http://marketplace.eclipse.org/content/simplegt.
   • To use the EMF-IncQuery implementation, install the EMF-IncQuery SDK and the Local Search Engine for EMF-IncQuery Developer Resources plug-ins from the http://download.eclipse.org/incquery/updates-extra/release update site. (This also installs the EMF-IncQuery Runtime. The complete list of EMF-IncQuery update sites is available at https://www.eclipse.org/incquery/download.php.)
   • To use the VIATRA implementation, install the VIATRA EMF Engine plug-in from http://download.eclipse.org/viatra2/emf/updates/release.

In general, we recommend to stick to your proven build solution, else you may spend a lot of time tinkering with the build. In theory, you can build Eclipse plug-ins with the Tycho Maven plug-in, however, it has a steep learning curve and is tricky to debug. For reference, see https://github.com/FTSRG/cheat-sheets/wiki/Maven-and-Eclipse.

Implementing the benchmark for a new tool

To implement a tool, it is recommended to start from an existing implementation. Please implement your own benchmark logic and benchmark case factory which instantiates the classes for each query defined in the benchmark.

Matches

In order to make the fewest assumptions on the specific implementations, the pattern matches are stored in the variable matches declared as a Collection<Object> (see the AbstractBenchmarkCase class). The framework requires the matches collection to be unique.

Comparators

To enable a consistent ordering of the matches, the framework requires a comparator class. Section 2.4.2 ("Ordering of the Match Set") in the case description defines the rules of the ordering.

For implementing a match comparator, we recommend two approaches:

• If the matches are represented in a tuple-like collection, they can be compared by iterating through the collection and comparing each elements in the tuple. Example: the EMFIncQueryBenchmarkComparator class.
• Use ComparisonChain class in Google Guava to compare the model elements in the matches. Example: the JavaRouteSensorMatchComparator class.

Naming conventions

To avoid confusion between the different implementations, we decided to use the Smurf Naming convention (see #21). This way, the classes in the Java implementation are named JavaBenchmarkCase, JavaPosLength, JavaPosLengthMatch, JavaPosLengthTransformation, while the classes in the EMF-IncQuery implementation are named EMFIncQueryBenchmarkCase, EMFIncQueryPosLength, etc. We found that relying on the package names to differentiate class names like

• hu.bme.mit.trainbenchmark.ttc.benchmark.java.BenchmarkCase and
• hu.bme.mit.trainbenchmark.ttc.benchmark.emfincquery.BenchmarkCase

is error-prone and should be avoided.

Troubleshooting

• Problem: if not running with Oracle JDK7, both the generation and the benchmarking freezes sometimes.
• Solution: see this issue for details.

Using EMF-IncQuery

To ensure that the Query Explorer works without launching a runtime Eclipse instance, go to Preferences, EMF-IncQuery and Query Explorer and tick the Dynamic EMF mode checkbox.