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To define properly datasets, the notion of sample should be introduced first. A sample is defined as polymorphic tuple. The size of the tuple will be by definition the arity of the sample itself.

A dataset is a collection of samples, that

Hence the dataset implements the notion of sequence.

The descriptive power of the datasets in Unit Test Framework comes from

[Tip] Tip

Only "monomorphic" datasets are supported, which means that all samples in a dataset have the same type and same arity [2] .

As we will see in the next sections, datasets representing collections of different types may be combined together (e.g.. zip or grid). These operations result in new datasets, in which the samples are of an augmented type.

Dataset interface

The interface of the dataset should implement the two following functions/fields:

Once a dataset class D is declared, it should be registered to the framework by specializing the class


with the condition that


evaluates to true.

The following example implements a custom dataset generating a Fibonacci sequence.

Example: Example of custom dataset


#define BOOST_TEST_MODULE dataset_example68
#include <boost/test/included/unit_test.hpp>
#include <boost/test/data/test_case.hpp>
#include <boost/test/data/monomorphic.hpp>
#include <sstream>

namespace bdata = boost::unit_test::data;

// Dataset generating a Fibonacci sequence
class fibonacci_dataset {
    // Samples type is int
    using sample=int;
    enum { arity = 1 };

    struct iterator {

        iterator() : a(1), b(1) {}

        int operator*() const   { return b; }
        void operator++()
            a = a + b;
            std::swap(a, b);
        int a;
        int b; // b is the output

    fibonacci_dataset()             {}

    // size is infinite
    bdata::size_t   size() const    { return bdata::BOOST_TEST_DS_INFINITE_SIZE; }

    // iterator
    iterator        begin() const   { return iterator(); }

namespace boost { namespace unit_test { namespace data { namespace monomorphic {
  // registering fibonacci_dataset as a proper dataset
  template <>
  struct is_dataset<fibonacci_dataset> : boost::mpl::true_ {};

// Creating a test-driven dataset 
    fibonacci_dataset() ^ bdata::make( { 1, 2, 3, 5, 8, 13, 21, 35, 56 } ),
    fib_sample, exp)
      BOOST_TEST(fib_sample == exp);


> example68
Running 9 test cases...
test.cpp(60): error: in "test1/_7": check fib_sample == exp has failed [34 != 35]
Failure occurred in a following context:
    fib_sample = 34; exp = 35;
test.cpp(60): error: in "test1/_8": check fib_sample == exp has failed [55 != 56]
Failure occurred in a following context:
    fib_sample = 55; exp = 56;

*** 2 failures are detected in the test module "dataset_example68"

[2] polymorphic datasets will be considered in the future. Their need is mainly driven by the replacement of the typed parametrized test cases by the dataset-like API.