SpECTRE  v2024.06.18
Writing Unit Tests

Unit tests are placed in the appropriate subdirectory of tests/Unit, which mirrors the directory hierarchy of src. Typically there should be one test executable for each production code library. For example, we have a DataStructures library and a Test_DataStructures executable. When adding a new test there are several scenarios that can occur, which are outlined below.

  • You are adding a new source file to an existing test library:
    If you are adding a new source file in a directory that already has a CMakeLists.txt simply create the source file, which should be named Test_ProductionCodeFileBeingTest.cpp and add that to the LIBRARY_SOURCES in the CMakeLists.txt file in the same directory you are adding the cpp file.
    If you are adding a new source file to a library but want to place it in a subdirectory you must first create the subdirectory. To provide a concrete example, say you are adding the directory TensorEagerMath to tests/Unit/DataStructures. After creating the directory you must add a call to add_subdirectory(TensorEagerMath) to tests/Unit/DataStructures/CMakeLists.txt before the call to add_test_library and after the LIBRARY_SOURCES are set. Next add the file tests/Unit/DataStructures/TensorEagerMath/CMakeLists.txt, which should add the new source files by calling set, e.g.
    The PARENT_SCOPE flag tells CMake to make the changes visible in the CMakeLists.txt file that called add_subdirectory. You can now add the Test_ProductionCodeFileBeingTested.cpp source file.
  • You are adding a new directory:
    If the directory is a new lowest level directory you must add a add_subdirectory call to tests/Unit/CMakeLists.txt. If it is a new subdirectory you must add a add_subdirectory call to the CMakeLists.txt file in the directory where you are adding the subdirectory. Next you should read the part on adding a new test library.
  • You are adding a new test library:
    After creating the subdirectory for the new test library you must add a CMakeLists.txt file. See tests/Unit/DataStructures/CMakeLists.txt for an example of one. The LIBRARY and LIBRARY_SOURCES variables set the name of the test library and the source files to be compiled into it. The library name should be of the format Test_ProductionLibraryName, for example Test_DataStructures. The library sources should be only the source files in the current directory. The add_subdirectory command can be used to add source files in subdirectories to the same library as is done in tests/Unit/CMakeLists.txt. The CMakeLists.txt in tests/Unit/DataStructures/TensorEagerMath is an example of how to add source files to a library from a subdirectory of the library. Note that the setting of LIBRARY_SOURCES here first includes the current LIBRARY_SOURCES and at the end specifies PARENT_SCOPE. The PARENT_SCOPE flag tells CMake to modify the variable in a scope that is visible to the parent directory, i.e. the CMakeLists.txt that called add_subdirectory.
    Finally, in the CMakeLists.txt of your new library you must call add_test_library. Again, see tests/Unit/DataStructures/CMakeLists.txt for an example. The add_test_library function adds a test executable with the name of the first argument and the source files of the third argument. Remember to use target_link_libraries to link any libraries your test executable uses (see Build System).

All tests must start with

// Distributed under the MIT License.
// See LICENSE.txt for details.
#include "Framework/TestingFramework.hpp"

The file tests/Unit/Framework/TestingFramework.hpp must always be the first include in the test file and must be separated from the STL includes by a blank line. All classes and free functions should be in an anonymous/unnamed namespace, e.g.

namespace {
class MyFreeClass {
/* ... */
void my_free_function() {
/* ... */
} // namespace

This is necessary to avoid symbol redefinition errors during linking.

Test cases are added by using the SPECTRE_TEST_CASE macro. The first argument to the macro is the test name, e.g. "Unit.DataStructures.Tensor", and the second argument is a list of tags. The tags list is a string where each element is in square brackets. For example, "[Unit][DataStructures]". The tags should only be the type of test, in this case Unit, and the library being tested, in this case DataStructures. The SPECTRE_TEST_CASE macro should be treated as a function, which means that it should be followed by { /* test code */ }. For example,

"[Unit][DataStructures]") {
CHECK("BlockLogical" == get_output(Frame::BlockLogical{}));
CHECK("ElementLogical" == get_output(Frame::ElementLogical{}));
CHECK("Grid" == get_output(Frame::Grid{}));
CHECK("Inertial" == get_output(Frame::Inertial{}));
CHECK("Distorted" == get_output(Frame::Distorted{}));
CHECK("NoFrame" == get_output(Frame::NoFrame{}));
std::string get_output(const T &t)
Get the streamed output of t as a std::string
Definition: GetOutput.hpp:14
Definition: IndexType.hpp:42
Definition: IndexType.hpp:47
Definition: IndexType.hpp:43
Definition: IndexType.hpp:45
Definition: IndexType.hpp:46
Represents an index that is not in a known frame, e.g. some internal intermediate frame that is irrel...
Definition: IndexType.hpp:50

From within a SPECTRE_TEST_CASE you are able to do all the things you would normally do in a C++ function, including calling other functions, setting variables, using lambdas, etc.

The CHECK macro in the above example is provided by Catch2 and is used to check conditions. We also provide the CHECK_ITERABLE_APPROX macro which checks if two doubles or two iterable containers of doubles are approximately equal. CHECK_ITERABLE_APPROX is especially useful for comparing Tensors, DataVectors, and Tensor<DataVector>s since it will iterate over nested containers as well.

Catch's CHECK statement only prints numbers out to approximately 10 digits at most, so you should generally prefer CHECK_ITERABLE_APPROX for checking double precision numbers, unless you want to check that two numbers are bitwise identical.

All unit tests must finish within a few seconds, the hard limit is 5, but having unit tests that long is strongly discouraged. They should typically complete in less than half a second. Tests that are longer are often no longer testing a small enough unit of code and should either be split into several unit tests or moved to an integration test.

Discovering New and Renamed Tests

When you add a new test to a source file or rename an existing test the change needs to be discovered by the testing infrastructure. This is done by building the target rebuild_cache, e.g. by running make rebuild_cache.

Testing Pointwise Functions

Pointwise functions should generally be tested in two different ways. The first is by taking input from an analytic solution and checking that the computed result is correct. The second is to use the random number generation comparison with Python infrastructure. In this approach the C++ function being tested is re-implemented in Python and the results are compared. Please follow these guidelines:

  • The Python implementation should be in a file with the same name as the source file that is being re-implemented and placed in the same directory as its corresponding Test_*.cpp source file.
  • The functions should have the same names as the C++ functions they re-implement.
  • If a function does sums over tensor indices then numpy.einsum should be used in Python to provide an alternative implementation of the loop structure.
  • You can import Python functions from other re-implementations in the tests/Unit/ directory to reduce code duplication. Note that the path you pass to pypp::SetupLocalPythonEnvironment determines the directory from which you can import Python modules. Either import modules directly from the tests/Unit/ directory (e.g. import PointwiseFunction.GeneralRelativity.Christoffel as christoffel) or use relative imports like from . import Christoffel as christoffel. Don't assume the Python environment is set up in a subdirectory of tests/Unit/.

It is possible to test C++ functions that return by value and ones that return by gsl::not_null. In the latter case, since it is possible to return multiple values, one Python function taking all non-gsl::not_null arguments must be supplied for each gsl::not_null argument to the C++. To perform the test the pypp::check_with_random_values() function must be called. For example, the following checks various C++ functions by calling into pypp:

&check_double_not_null2<T>, "PyppPyTests",
{"check_double_not_null2_result0", "check_double_not_null2_result1"},
{{{0.0, 10.0}, {-10.0, 0.0}}}, value);
constexpr T & value(T &t)
Returns t.value() if t is a std::optional otherwise returns t.
Definition: OptionalHelpers.hpp:32

The corresponding Python functions are:

def check_double_not_null2_result0(t0, t1):
return np.sqrt(t0) + 1.0 / np.sqrt(-t1)
def check_double_not_null2_result1(t0, t1):
return 2.0 * t0 + t1

Writing and Fixing Random-Value Based Tests

Many tests in SpECTRE make use of randomly generated numbers in order to increase the parameter space covered by the tests. The random number generator is set up using:

#define MAKE_GENERATOR(...)
MAKE_GENERATOR(NAME [, SEED]) declares a variable of name NAME containing a generator of type std::mt...
Definition: TestHelpers.hpp:419

The generator gen can then be passed to distribution classes such as std::uniform_real_distribution or UniformCustomDistribution.

Each time the test is run, a different random seed will be used. When writing a test that uses random values, it is good practice to run the test at least \(10^4\) times in order to set any tolerances on checks used in the test. This can be done by using the following command in the build directory (SPECTRE_BUILD_DIR):

ctest --repeat-until-fail 10000 -R TEST_NAME

where TEST_NAME is the test name passed to SPECTRE_TEST_CASE (e.g. Unit.Evolution.Systems.CurvedScalarWave.Characteristics).

If a test case fails when using a random number generated by MAKE_GENERATOR, as part of the output from the failed test will be the text


Note that the output of tests can be found in SPECTRE_BUILD_DIR/Testing/Temporary/LastTest.log

The failing test case can then be reproduced by changing MAKE_GENERATOR call at the provided line in the given file to


If the MAKE_GENERATOR is within CheckWithRandomValues.hpp, the failing test case most likely has occurred within a call to pypp::check_with_random_values(). In such a case, additional information should have been printed to help you determine which call to pypp::check_with_random_values() has failed. The critical information is the line


where FUNCTION_NAME should correspond to the third argument of a call to pypp::check_with_random_values(). The seed that caused the test to fail can then be passed as an additional argument to pypp::check_with_random_values(), where you may also need to pass in the default value of the comparison tolerance.

Typically, you will need to adjust a tolerance used in a CHECK somewhere in the test in order to get the test to succeed reliably. The function pypp::check_with_random_values() takes an argument that specifies the lower and upper bounds of random quantities. Typically these should be chosen to be of order unity in order to decrease the chance of occasionally generating large numbers through multiplications which can cause an error above a reasonable tolerance.

Testing Failure Cases

ASSERTs and ERRORs can be tested with the CHECK_THROWS_WITH macro. This macro takes two arguments: the first is either an expression or a lambda that is expected to trigger an exception (which now are thrown by ASSERT and ERROR (Note: You may need to add () wrapping the lambda in order for it to compile.); the second is a Catch Matcher (see Catch2 for complete documentation), usually a Catch::Matchers::ContainsSubstring() macro that matches a substring of the error message of the thrown exception.

When testing ASSERTs the CHECK_THROWS_WITH should be enclosed between #ifdef SPECTRE_DEBUG and an #endif If the #ifdef SPECTRE_DEBUG block is omitted then compilers will correctly flag the code as being unreachable which results in warnings.

Adding the "attribute" // [[OutputRegex, Regular expression to match]] before the SPECTRE_TEST_CASE macro will force ctest to only pass the particular test if the regular expression is found in the output of the test. In this case, the first line of the test should call the macro OUTPUT_TEST();.

Testing Actions

The action testing framework is documented as part of the ActionTesting namespace.

Input file tests

We have a suite of input file tests in addition to unit tests. Every input file in the tests/InputFiles/ directory is added to the test suite automatically. The input file must specify the Executable it should run with in the input file metadata (above the --- marker in the input file). Properties of the test are controlled by the Testing section in the input file metadata. The following properties are available:

  • Check: Semicolon-separated list of checks, e.g. parse;execute. The following checks are available:
    • parse: Just check that the input file passes option parsing.
    • execute: Run the executable. If the input file metadata has an ExpectedOutput field, check that these files have been written. See spectre.tools.CleanOutput for details.
    • execute_check_output: In additional to execute, check the contents of some output files. The checks are defined by the OutputFileChecks in the input file metadata. See spectre.tools.CheckOutputFiles for details.
  • CommandLineArgs (optional): Additional command-line arguments passed to the executable.
  • ExpectedExitCode (optional): The expected exit code of the executable. Default: 0. See Parallel::ExitCode for possible exit codes.
  • Timeout (optional): Timeout for the test. Default: 2 seconds.
  • Priority (optional): Priority of running this test on CI. Possible values are: low (not usually run on CI), normal (run at least once on CI), high (run always on CI). Default: normal.