Testing

There are three types of test used in BOUT++, in order of complexity: unit tests, integrated tests, and “method of manufactured solutions” (MMS) tests. Unit tests are very short, quick tests that test a single “unit” – usually a single function or method. Integrated tests are longer tests that range from tests that need a lot of set up and check multiple conditions, to full physics model tests. MMS tests check the numerical properties of operators, such as the error scaling of derivatives.

There is a test suite that runs through all of the unit tests, and selected integrated and MMS tests. The easiest way to run this is with:

$ make check

We expect that any new feature or function implemented in BOUT++ also has some corresponding tests, and strongly prefer unit tests.

Automated tests and code coverage

BOUT++ uses Travis CI to automatically run the test suite on every push to the GitHub repository, as well as on every submitted Pull Request. The Travis settings are in .travis.yml. Pull requests that fail the tests will not be merged.

We also gather information from how well the unit tests cover the library using CodeCov, the settings for which are stored in .codecov.yml.

Unit tests

The unit test suits aims to be a comprehensive set of tests that run very fast and ensure the basic functionality of BOUT++ is correct. At the time of writing, we have around 500 tests that run in less than a second. Because these tests run very quickly, they should be run on every commit (or even more often!). For more information on the unit tests, see tests/unit/README.md.

You can run the unit tests with:

$ make check-unit-tests

Integrated tests

This set of tests are designed to test that different components of the BOUT++ library work together. These tests are more expensive than the unit tests, but are expected to be run on at least every pull request, and the majority on every commit.

You can run the integrated tests with:

$ make check-integrated-tests

The test suite is in the tests/integrated directory, and is run using the test_suite python script. tests/integrated/test_suite_list contains a list of the subdirectories to run (e.g. test-io, test-laplace, interchange-instability). In each of those subdirectories the script runtest is executed, and the return value used to determine if the test passed or failed.

All tests should be short, otherwise it discourages people from running the tests before committing changes. A few minutes or less on a typical desktop, and ideally only a few seconds. If you have a large simulation which you want to stop anyone breaking, find starting parameters which are as sensitive as possible so that the simulation can be run quickly.

Custom test requirements

Some tests require particular libraries or environments, so should be skipped if these are not available. To do this, each runtest script can contain a line starting with #requires, followed by a python expression which evaluates to True or False. For example, a test which doesn’t work if both ARKODE and PETSc are used:

#requires not (arkode and petsc)

or if there were a test which required PETSc to be available, it could specify

#requires petsc

Currently the requirements which can be combined are travis, netcdf, pnetcdf, hdf5, pvode, cvode, ida, lapack, petsc, slepc, mumps, arkode, openmp and make. The make requirement is set to True when the tests are being compiled (but not run), and False when the scripts are run. It’s used for tests which do not have a compilation stage.

Method of Manufactured Solutions

The Method of Manufactured solutions (MMS) is a rigorous way to check that a numerical algorithm is implemented correctly. A known solution is specified (manufactured), and it is possible to check that the code output converges to this solution at the expected rate.

To enable testing by MMS, switch an input option “mms” to true:

[solver]
mms = true

This will have the following effect:

  1. For each evolving variable, the solution will be used to initialise and to calculate the error
  2. For each evolving variable, a source function will be read from the input file and added to the time derivative.

Note

The convergence behaviour of derivatives using FFTs is quite different to the finite difference methods: once the highest frequency in the manufactured solution is resolved, the accuracy will jump enormously, and after that, finer grids will not increase the accuracy. Whereas with finite difference methods, accuracy varies smoothly as the grid is refined.

Choosing manufactured solutions

Manufactured solutions must be continuous and have continuous derivatives. Common mistakes:

  • Don’t use terms multiplying coordinates together e.g. x * z or y * z. These are not periodic in \(y\) and/or \(z\), so will give strange answers and usually no convergence. Instead use x * sin(z) or similar, which are periodic.

Timing

To time parts of the code, and calculate the percentage of time spent in communications, file I/O, etc. there is the Timer class defined in include/bout/sys/timer.hxx. To use it, just create a Timer object at the beginning of the function you want to time:

#include <bout/sys/timer.hxx>

void someFunction() {
  Timer timer("test")
  ...
}

Creating the object starts the timer, and since the object is destroyed when the function returns (since it goes out of scope) the destructor stops the timer.

class Timer {
public:
  Timer();
  Timer(const std::string &label);
  ~Timer();

  double getTime();
  double resetTime();
};

The empty constructor is equivalent to setting label = "" . Constructors call a private function getInfo() , which looks up the timer_info structure corresponding to the label in a map<string, timer_info*> . If no such structure exists, then one is created. This structure is defined as:

struct timer_info {
  double time;    ///< Total time
  bool running;   ///< Is the timer currently running?
  double started; ///< Start time
};

Since each timer can only have one entry in the map, creating two timers with the same label at the same time will lead to trouble. Hence this code is not thread-safe.

The member functions getTime() and resetTime() both return the current time. Whereas getTime() only returns the time without modifying the timer, resetTime() also resets the timer to zero.

If you don’t have the object, you can still get and reset the time using static methods:

double Timer::getTime(const std::string &label);
double Timer::resetTime(const std::string &label);

These look up the timer_info structure, and perform the same task as their non-static namesakes. These functions are used by the monitor function in bout++.cxx to print the percentage timing information.