Contributing to VOC

If you experience problems with VOC, log them on GitHub.

If you want to contribute code, please fork the code and submit a pull request.

If you’re a newcomer to the project looking for how to start contributing, you may find useful the First Timers Guide.

Setting up your development environment

The process of setting up a development environment is very similar to the /intro/getting-started process. The biggest difference is that instead of using the official PyBee repository, you’ll be using your own Github fork.

As with the getting started guide, these instructions will assume that you have Python 3.4+, a Java 7 or Java 8 JDK, and Apache ANT installed.

Note: If you are on Linux, you will need to install an extra package to be able to run the test suite.
  • Ubuntu 12.04 and 14.04: libpython3.4-testsuite This can be done by running apt-get install libpython3.4-testsuite.
  • Ubuntu 16.04 and 16.10: libpython3.5-testsuite This can be done by running apt-get install libpython3.5-testsuite.

Start by forking VOC into your own Github repository; then check out your fork to your own computer into a development directory:

$ mkdir voc-dev
$ cd voc-dev
$ git clone git@github.com:<your github username>/voc.git

Then create a virtual environment and install VOC into it:

$ python3 -m venv env
$ . env/bin/activate
$ cd voc
$ pip install -e .

For Windows the use of cmd under Administrator permission is suggested instead of PowerShell.

> py -3 -m venv env
> env\Scripts\activate.bat
> cd voc
> pip install -e .

You’re now ready to run the test suite!

Running the test suite

To run the entire test suite, type:

$ python setup.py test

To capture unexpected successes and new failures in test:

$ python setup.py test 2>&1 | grep -E 'success|FAIL'

Running the full test suite will take quite a while - it takes 40 minutes on the CI server. You can speed this up by running the tests in parallel via pytest:

$ pip install -r requirements/tests.txt
$ py.test -n auto

You can specify the number of cores to utilize, or use auto as shown above to use all available cores.

If you just want to run a single test, or a single group of tests, you can provide command-line arguments.

To run a single test, provide the full dotted-path to the test:

$ python setup.py test -s tests.datatypes.test_str.BinaryStrOperationTests.test_add_bool

To run a full test case, do the same, but stop at the test case name:

$ python setup.py test -s tests.datatypes.test_str.BinaryStrOperationTests

Or, to run all the Str datatype tests:

$ python setup.py test -s tests.datatypes.test_str

Or, to run all the datatypes tests:

$ python setup.py test -s tests.datatypes

Or you can use Cricket, a GUI tool for running test suites. To start cricket in the background:

$ pip install -r requirements/tests.txt
$ cricket-unittest &

This should open a GUI window that lists all the tests. From there you can “Run all” or select specific tests and “Run selected.”

Running the code style checks

Before sending your pull request for review, you may want to run the style checks locally.

These checks also run automatically in Travis, but you will avoid unnecessary waiting time if you do this beforehand and fix your code to follow the style rules.

In order to do that, first you need to install flake8:

pip install flake8

Then, whenever you want to run the checks, run the following command inside the project’s directory:

flake8 && ant checkstyle

Working with code for Java bytecode

If you find yourself needing to work with the parts of VOC that generates Java bytecode, you might find helpful these pointers:

  • A Python interpreter written in Python will get you started on how stack based machines work. While the examples aren’t for the JVM, the workings of the machines are similar enough to help you get used to the thinking.
  • The Java bytecode instructions are represented by classes in voc.java.opcodes that inherit from voc.java.opcodes.Opcode. Most of the code to generate bytecode is in the voc.python.ast module, and the bytecode generating code is often a sequence of instances of these opcode classes calling the method add_opcodes() for the current context.
  • The add_opcodes() method also support helpers that work as pseudo-instructions, which allow to generate more complex sequences of instructions, like the IF(), TRY(), CATCH() from the voc.voc.python.structures module. It’s easier to understand how these work finding an example of usage in VOC itself. Ask in Gitter, if you need help with it.

Troubleshooting generated bytecode

Troubleshooting issues in the generated bytecode can be a bit hard.

There are some tools that can help you to see what’s going on. You can use a tool available in the ASM project to check the bytecode for problems.

Download the ASM binary distribution from the ASM project, extract the file in some directory and create a script like this:

ASM_VERSION=5.2
ASM_HOME=/path/to/asm-${ASM_VERSION}/lib

[ -n "$2" ] || { echo "Usage: $(basename $0) CLASSPATH CLASS_TO_ANALYSE"; exit 1; }

asm_file="$ASM_HOME/asm-${ASM_VERSION}.jar"
[ -f "$asm_file" ] ||  { echo "Couldn't find file $asm_file"; exit 1; }

classpath=$1
class_to_analyse=$2

java -cp "$ASM_HOME/asm-${ASM_VERSION}.jar:$ASM_HOME/asm-tree-${ASM_VERSION}.jar:$ASM_HOME/asm-analysis-${ASM_VERSION}.jar:$ASM_HOME/asm-util-${ASM_VERSION}.jar:$classpath" org.objectweb.asm.util.CheckClassAdapter $class_to_analyse

Then you can call it like:

asm.sh /PATH/TO/voc/dist/python-java-support.jar:. path.to.JavaClass

This will give you a brief diagnosis of problems found in the bytecode for the given Java class, and if possible will print a friendlier version of the bytecode.

If you just want to see a human friendly version of the Java bytecode to double check the generated code, you can also try the command:

javap -c path.to.JavaClass