Data-Driven Code Generation of Unit Tests Part 2: C++, CMake, Jinja2, Boost

This blog post explains how I used CMake, Jinja2, and the Boost Unit Test framework to perform data-driven code generation of unit tests for a financial performance analytics library.  If you haven’t read it already, I recommend starting with Part 1: Background.

All performance analytics metadata is stored in a single metadata file called metadata.csv.  This file contains the complete list of calculations, and for each calculation, its settings (i.e. how it differs from other calculations), including properties like:

  1. How many parameters does the calculation take (1, 2, or 3)?
  2. Does the calculation have an online (streaming) implementation?
  3. Does the calculation support annualization?
  4. What is the default annualization mode?
  5. Given a predefined set of inputs, what are the expected values of the calculation for various combinations of time period, annualization, etc.

The file looks something like:

algorithm_type,function_name,num_parameters,minimum_arr_size,supports_streaming,supports_annualization,default_annualization,expected_value_unannualized,expected_value_annualized_daily,expected_value_annualized_weekly,expected_value_annualized_monthly,expected_value_annualized_quarterly,expected_value_annualized_semiannually,expected_value_annualized_daily_200_day_year
absolute_statistics,calculation1,1,1,true,false,never,7.283238516,-999,-999,-999,-999,-999,-999
...
relative_statistics,calculation2,3,1,true,true,always,0.189846006,69.34125385,9.871992334,2.278152077,0.759384026,0.379692013,37.96920129
...

I use CSV rather than JSON or YAML because it can be easily read by CMake during the build process (more below).

A Jinja2 template defines all unit tests for a given calculation.  It uses the attributes found in metadata.csv to determine how to generate the appropriate source code.  For example, if the calculation does not support annualization per the supports_annualization flag, the Jinja2 template will ignore (not generate) the unit tests which test annualization support.

Each calculation has a number of possible combinations to test for, such as:

  1. Test the online vs. offline versions of the calculation
  2. Test the various annualization settings (always, never, calculation default)
  3. Test the various pre-defined annualization periods (daily, weekly, monthly, etc.)
  4. etc.

The Jinja2 template uses for loops extensively to make sure that it tests all possible combinations of all of the above parameters. It looks something like:

{% for calc_type in calc_types %}
{% for annualize in annualizes %}
{% for frequency in frequencies %}

BOOST_AUTO_TEST_CASE(test_{{ function_name }}_{{calc_type}}_annualize_{{annualize}}_frequency_{{frequency}})
{
    ....
}

{% endfor %}
{% endfor %}
{% endfor %}

As you can imagine, the resulting code coverage of the unit tests is excellent.

A Python script, render_jinja.py, knows how to read metadata.csv and pass the appropriate values to Jinja2 in order to generate the unit tests for a given function.  The meat of the Python script looks like:

function_name = ...
output_file = ...
template_file = ...

with open('../../metadata.csv', 'r') as f:
    mr = csv.DictReader(row for row in f if not row.startswith('#'))
    for row in mr:
        if row['function_name'] == function_name:
            fn_metadata = row
            break

# Generate unit test template
env = jinja2.Environment(loader=jinja2.FileSystemLoader('.'), trim_blocks=True)
template = env.get_template(template_file)
result = template.render(fn_metadata)
output_file.write(result)

The build system uses CMake.  It too reads metadata.csv to get a list of calculations, calls render_jinja.py on each calculation to generate the unit test code C++ file, and then compiles and executes the unit tests. Here’s a sample of the CMake build file:

cmake_minimum_required(VERSION 2.8)
project(perfanalytics-cpp-test)

enable_testing()

if (WIN32)
  add_definitions(-DBOOST_ALL_NO_LIB)
  set(Boost_USE_STATIC_LIBS ON)
else()
  add_definitions(-DBOOST_TEST_DYN_LINK)
endif()
find_package(Boost COMPONENTS unit_test_framework REQUIRED)

set(TEST_COMMON_SRC memory_stream.cpp)

# Populate CALC_NAMES from metadata.csv
file(STRINGS ${CMAKE_CURRENT_SOURCE_DIR}/metadata.csv CALC_METADATA)
set(index 1)
list(LENGTH CALC_METADATA COUNT)
while(index LESS COUNT)
  list(GET CALC_METADATA ${index} line)

  if (NOT "${line}" MATCHES "^#")
    # convert line to a CMake list
    string(REPLACE "," ";" l ${line})
    list(GET l 1 calc_name)
    list(GET l 3 supports_streaming)
    list(APPEND CALC_NAMES ${calc_name})
    list(APPEND CALC_SUPPORTS_STREAMING ${supports_streaming})
  endif()

  math(EXPR index "${index}+1")
endwhile(index LESS COUNT)

# Note how we generate source into the binary directory.  This
# is important -- generated source is *output*, not source,
# and should not be checked into source control.
foreach(fn ${CALC_NAMES})
  add_custom_command(
    OUTPUT ${CMAKE_CURRENT_BINARY_DIR}/generated/${fn}_unit_test.cpp
    COMMAND python ${CMAKE_CURRENT_SOURCE_DIR}/render_jinja.py -o ${CMAKE_CURRENT_BINARY_DIR}/generated/${fn}_unit_test.cpp -f ${fn} -t unit_test_template.cpp.j2
    DEPENDS ${CMAKE_CURRENT_SOURCE_DIR}/render_jinja.py ${CMAKE_CURRENT_SOURCE_DIR}/unit_test_template.cpp.j2 ${CMAKE_CURRENT_SOURCE_DIR}/../../metadata.csv
    WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
    COMMENT "Generating C++ unit test ${CMAKE_CURRENT_BINARY_DIR}/generated/${fn}_unit_test.cpp"
  )
  list(APPEND TESTCASE_SRC ${CMAKE_CURRENT_BINARY_DIR}/generated/${fn}_unit_test.cpp)
endforeach()

foreach (testSrc ${TESTCASE_SRC})
  get_filename_component(testName ${testSrc} NAME_WE)

  # Test against static library
  add_executable(cpp_static_${testName} ${testSrc} ${TEST_COMMON_SRC})
  target_link_libraries(cpp_static_${testName} perfanalytics_cpp_static ${Boost_LIBRARIES})
  add_test(NAME cpp_static_${testName} COMMAND cpp_static_${testName})

  # Test against shared library
  if (BUILD_SHARED_LIBRARY)
    add_executable(cpp_shared_${testName} ${testSrc} ${TEST_COMMON_SRC})
    target_link_libraries(cpp_shared_${testName} perfanalytics_cpp_shared ${Boost_LIBRARIES})
    add_test(NAME cpp_shared_${testName} COMMAND cpp_shared_${testName})
  endif()
endforeach(testSrc)

A single script, build.sh, ties everything together.  While the full build.sh supports a number of command-line options (e.g. -c, --clean for a clean build; -d, --debug for a debug build; -r, --release for a release build), the core of the script looks like:

set BUILD_TYPE=Debug # or Release
if [ ! -d $BUILD_TYPE ]; then mkdir $BUILD_TYPE; fi
cd $BUILD_TYPE
cmake .. -DCMAKE_BUILD_TYPE=$BUILD_TYPE
cmake --build . --config $BUILD_TYPE
env CTEST_OUTPUT_ON_FAILURE=1 ctest -C $BUILD_TYPE
cpack -C $BUILD_TYPE

Windows uses an equivalent script called build.cmd.

I am quite happy with the results.  Adding a new calculation is almost as simple as writing the implementation of the calculation and adding a single line to metadata.csv. The unit tests are comprehensive and provide great code coverage.  New test patterns (e.g. what should happen if you pass in NULL to a calculation?) can be added to all calculations at once, simply by editing the Jinja2 template file. Everything works across Windows, Mac OS, and Linux.

The only remaining frustration that I have is that the build system will often re-generate the unit test source code, and recompile the unit tests, even though nothing has changed. This notably slows down build times.  I’m hopeful this can be solved with some further work on the CMake build file, but I’ll leave that for another time.

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2 thoughts on “Data-Driven Code Generation of Unit Tests Part 2: C++, CMake, Jinja2, Boost

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