Python Code Inspection with SonarQube

By Craig Thomas, Mon 02 June 2014, in category Build tools

python, sonarqube

Last time, I wrote about setting up Travis CI to effortlessly perform continuous integration and testing. My next step was to determine what was actually being tested in my Python Chip 8 emulator, and improve upon areas that had insufficient tests to cover them. While code coverage isn’t the best metric for code quality, it does at least provide visibility on what you are not actually testing.

SonarQube

A good tool that allows you to inspect your code is SonarQube (previously just called Sonar). Available as a standalone server, SonarQube gets you up and analyzing code in minutes. By default, the out-of-the-box configuration provides an H2 on-disk database that isn’t rated for production, but doesn't require any external dependencies (like PostgreSQL or MySQL). To get it up and running, simply unzip it and run it. On Linux this looks like:

unzip sonarqube-4.3.zip
./sonarqube-4.3/bin/linux-x86-64/sonar.sh start

This will start SonarQube on your local machine, and set the server listening on port 9000. To get to it, simply browse to http://localhost:9000 in your web browser. When you initially visit the link, there will be no projects loaded. To load your project in to SonarQube, you will need one additional piece of software - the Sonar Runner.

Sonar Runner

The Sonar Runner is responsible for running your test cases and posting the results to SonarQube. In order for the Sonar Runner to be able to know what to do, each project needs a file called sonar-project.properties (unless you are using a Maven module - but that's for a future post!). The properties file stores some pretty standard stuff:

sonar.projectKey=chip8:python
sonar.projectName=Chip8 Python
sonar.projectVersion=1.0
sonar.sources=chip8
sonar.tests=test
sonar.language=py
sonar.sourceEncoding=UTF-8
sonar.python.xunit.reportPath=nosetests.xml
sonar.python.coverage.reportPath=coverage.xml
sonar.python.coveragePlugin=cobertura

The sonar.project* should be fairly self-explanatory. The sonar.sources property tells Sonar Runner where the source files for the project are located. The sonar.tests property tells Sonar Runner where the unit tests are located. The last three lines tell the Sonar Runner where to find the coverage reports, and what format to find them in. Note that both the sonar.sources and sonar.tests property need to point to different sub-directories. If you keep them in the same directory, you will get errors such as:

ERROR: Error during Sonar runner execution
ERROR: Unable to execute Sonar
ERROR: Caused by: File [relative=chip8/screen.py, abs=/export/disk1/emulators/python/chip8/chip8/screen.py] can't be indexed twice. Please check that inclusion/exclusion patterns produce disjoint sets for main and test files

Nosetests and Coverage

Okay, so with Sonar Runner configured, we need one more tool. Sonar Runner by itself will not run the unit tests and gather coverage information. We need to use another package called nosetests. Nose is an advanced test runner that can easily be installed with pip. We also need the coverage tool. These can be installed with:

pip install nose
pip install coverage

Once installed, you need to run nosetests to run the unit tests for you, and generate information relating to the source code. The following line runs the test runner, generates coverage information, and generates an XML report that Sonar Runner will use:

nosetests --with-coverage --cover-package=chip8 --cover-branches --cover-xml

Note the --cover-package option. This restricts the coverage module to the chip8 directory - without it, every single Python source file will be included in the coverage report.

Putting It All Together

With SonarQube, Sonar Runner, and Nose, you are now ready to start inspecting your code. A typical session would be to make some changes to a source file, then run the following:

nosetests --with-coverage --cover-package=chip8 --cover-branches --cover-xml
sed -i 's/filename="/filename=".\//g' coverage.xml
sonar-runner

The nosetests line we have seen before. But what’s with the sed command? As I discovered, it's a work-around to making the Sonar Runner properly identify the file names in the coverage.xml file. Without it, the Sonar Runner will discard the coverage metrics for the files in the chip8 package (see here and here for more information and discussion). Finally, the sonar-runner command will execute the runner and post the results. Once again visit http://localhost:9000 to see changes to your project.

Interpreting the Results

As I mentioned before, testing using coverage as the guiding principle isn't the best way to ensure you've tested everything. For a good run-down of why this is the case, check out Ned Batchelder’s talk at PyCon 2009 called Coverage testing, the good and the bad.

As a blunt tool, SonarQube can at least tell you what you're not testing. On the dashboard for the project, you can see metrics related to the unit test coverage and the unit test success:

Unit Test Coverage Overview

Clicking on the unit tests coverage report will display the coverage breakdown per module. Clicking on a module will provide coverage details for that file. For example, checking out the CPU code for the Python emulator, we see that the code coverage is 69.6%:

Example Coverage Report

The green lines at the left hand side of the code listing represent lines of code that have been covered by running the unit test suite. Scrolling down a little in the code, the functions execute_instruction and execute_logical_instruction were not tested by the unit test suite. SonarQube nicely highlights these areas in pink so that we can quickly see what we're not testing:

Missing Coverage Example

Now it's up to me to go back and write tests to ensure that those functions are working as expected. Then, I can re-run the test suite, and perform another SonarQube analysis on the code.

Conclusion

SonarQube and the Sonar Runner provide a simple and effective way to inspect what your unit tests are actually testing with only a few extra packages. This only scratches the surface of what SonarQube can actually do. In a future post, I will examine some of the other SonarQube metrics, and how they can help improve code quality.