md__chakra_desktop_cipra_src_cipra-1.2.1_doc_tutorial(3)

NAME
md__chakra_desktop_cipra_src_cipra-1.2.1_doc_tutorial - Tutorial Modern software development knows the benefits of automated testing over (or as a complement to) dedicated QA staff. Automated testing gives a quicker bug cycle for developers and allows them to catch bugs introduced in code changes by other modules or modifications to the same module. Indeed, automated unit tests are a corner stone of Extreme Programming, and many software projects don't allow new code to be checked in without accompanying automated tests.

On the other hand, most C++ unit testing frameworks are heavy-weight and restricting. They are filled with unnecessary macros which can present problems, and they are often not thread-safe, which makes testing concurrent code even more difficult.

cipra was designed as a response to this, with the following goals in mind:

1.  Be simple. The API of cipra closely mirrors that of Perl's Test::More, a widely-used testing module in the Perl community that elegantly works well in C++.

2.  Be flexible. We don't want to design the user's tests for them. Instead of forcing the user's tests to follow a 'Setup-Test-Teardown' structure, which can clash with popular C++ idioms like RAII and threading, we let the user of cipra manage this themselves only if they need it.

3.  Use Modern C++. Although we are using Perl's Test::More module as a guide, cipra is thoroughly a C++ library. Written in C++11, it eschews macros and is thread-safe, using modern C++ techniques that aren't intrusive on the code you want to test. We don't require anything other than a C++11 compiler.

This tutorial should give you an introduction to using cipra. It expects C++11 experience, but doesn't require any knowledge of Test::More or Perl testing.

Getting Started with cipra

cipra is a header-only library, which means you don't have to compile anything to use it. Simply install the header files to your system or package them in your project, and you can start using cipra as a test framework. Make sure you are using a C++11 compliant compiler.

You will need a TAP13 test harness to track and pretty-print the output of cipra test programs. Right now, the best option is to use the prove program that comes with Perl. In a bind, the following UNIX command can also be used to check for failures, assuming your test program is named test-program.t:

$ ./test-program.t | grep "^not ok"

In the future, cipra will provide a test harness written entirely in C++11, making cipra tests dependency-free. In this tutorial, we will be showing the output of cipra itself, not run through any test harness.

Let's start with a very simple test program using cipra. We'll assume that the header files are in your system include path (placed in /usr/include, for instance, or compiled in with a -I flag on GCC). Start off by including cipra.h:

#include <cipra.h>

In cipra, all test programs need to have a class derived from cipra::fixture, which contains all the test functions in cipra. The actual tests are contained in the overridden test() function. Let's make a class that will contain our tests and then make an instance of this class:

struct my_fixture : cipra::fixture {
    virtual void test() override {
        /* tests ... */
    }
} test;

By calling the run() method of test, we get the return value of the test, running all the test functions and then quitting.

return test.run();

Inside the test() function, we need to declare how many tests we are planning to run, just in case we exit from the test prematurely. For our first test program, we'll just have a single test, so we call the plan() function at the beginning of the test() function with the argument 1.

plan(1);

We're finally ready to actually run a test. It will be a simple test, making sure that 5 == 5 is true. To do this, we use cipra's most basic test function template, ok(), which is declared as follows:

template<typename funcT>
    void ok(funcT expr, std::string name = std::string(""));

Here, funcT is anything that has an operator() whose return value is convertible to a bool. This could be a function pointer, a functor, the result of std::bind(), or a C++11 lambda. Instead of declaring our test code separately, we prefer to use lambdas and declare the test directly in the call to ok(). The second argument is a description of the test that will be printed with the test output. When reading the test output at a glance, it's much easier to see which test is failing if you include these descriptions. We recommend that you always do this. Let's write a call to ok() that checks whether 5 == 5:

ok([](){ return 5 == 5; }, "Five is equal to five.");

Here is the full code of our first test file:

#include <cipra.h>
int main(int argc, char* argv[]) {
    struct my_fixture : cipra::fixture {
        virtual void test() override {
            plan(1);
            ok([](){ return 5 == 5; }, "Five is equal to five.");
        }
    } test;
    return test.run();
}

If we run the above code, we get the following output:

1..1
ok 1 - Five is equal to five.

This output is in a format called TAP13, short for the 'Test Anything Protocol, version 13'. TAP is standard format used by many test harnesses, especially those within the Perl community. Test::More, the Perl module on which cipra is based, uses TAP. Because of the wide support for TAP, cipra chooses it as a standard output format.

For comparison, here is a Perl script using Test::More that performs the same test:

#!/usr/bin/env perl -w
use Test::More => 1; # or Test::Simple
ok(5 == 5, 'Five is equal to five.');

The C++ code using cipra does contain a lot of boilerplate code compared with the Perl code using Test::More. Ideally, this code should be reduced. The goal for a future version of cipra is to reduce the code to only the following:

#include <cipra.h>
int main(int argc, char* argv[]) {
    plan(1);
    ok([](){ return 5 == 5; }, "Five is equal to five.");
    return done();
}

In real testing environments, though, tests fail. Let's add a failing test:

#include <cipra.h>
int main(int argc, char* argv[]) {
    struct my_fixture : cipra::fixture {
        virtual void test() override {
            plan(2); // change the test plan!
            ok([](){ return 5 == 5; }, "Five is equal to five.");
            ok([](){ return 5 == 6; }, "Five is equal to six.");
        }
    } test;
    return test.run();
}

The expression 5 == 6 is obviously false, so this test should fail. If we run the test program, we'll get the following output:

1..2
ok 1 - Five is equal to five.
not ok 2 - Five is equal to six.

This is TAP's way of telling us that the test failed.

More Complex Test Plans

So far, we've called the plan() function using an integer argument, indicating the number of test functions that we are expecting to call in the test program. This function is responsible for the 1..n line in the above output, where n is the number you pass in. That line is called the test plan in TAP terms. By including it, you declare to the test harness how many tests you are expecting to run, so it can tell whether the test was aborted prematurely.

In our tests so far, the test plan has come at the very beginning of the output, before any of the test functions ran. Sometimes, though, you don't know how many tests are going to run. Consider the following test program snippet:

plan(/* ??? */);
std::ifstream file("data-file.txt");
std::string line;
int num_lines;
ok [&](){ file >> num_lines; return true; }, "Read the line count.");
// now we know the number of tests!
while (file >> line) {
  ok([=](){ is_valid_line(line); }, "The line is valid.");
}

We don't know how many tests should have run until after the file has been read. In this case, we can put the test plan at the end of the test. This does not mean that the test plan can go anywhere in the test program. The test plan must come at either the beginning or the end of the test output. We modify our code and get the right plan:

std::ifstream file("data-file.txt");
std::string line;
int num_lines;
ok [&](){ file >> num_lines; return true; }, "Read the line count.");
// now we know the number of tests!
while (file >> line) {
  ok([=](){ is_valid_line(line); }, "The line is valid.");
}
plan(num_lines + 1);

and the output

ok 1 - Read the line count.
ok 2 - The line is valid.
ok 3 - The line is valid.
ok 4 - The line is valid.
...
ok 29 - The line is valid.
1..29

If we want to skip all the tests in the test program (for example, if the test is testing behavior that isn't implemented yet), we can call the function with skip_all as the argument. It only makes sense to do this before all test functions.

Diagnostics

TAP supports outputting information from the test program to the test harness that won't be interpreted, but will instead be considered comments to be displayed to the user. cipra implements these with the diag() and note() functions, as well as the explain() function template.

void diag(std::string message);
void note(std::string message);
template <typename T>
    void explain(T object);

The difference between diag() and note() is subtle. While both output a TAP diagnostic, diag() prints the message to stderr and note() prints the message to stdout. Consider the following code:

plan(0); // we don't have any test functions!
note("This is a diagnostic message!")

The output of this code will be

1..0
# This is a diagnostic message!

If you want to pretty-print an object using its streaming operator<<(), use the function template explain(). You can also specialize the cipra::print_trait template to use a different function.

Convenience Test Functions

As we stated earlier, the ok() function is the only test function you really need. Every one of the other test functions in cipra could be replaced by calls to ok(). These other test functions are simply conveniences that express intent. Because they express intent, they can make the test source code easier to read and can provide better diagnostic information when the test fails.

The two most simple test functions are pass() and fail(). Their signatures are below:

void pass(std::string name = std::string(""));
void fail(std::string name = std::string(""));

The the function call pass(name) is equivalent to ok(true, name), while the function call fail(name) is equivalent to ok(false, name).

Another test function template is is(), which compares two values with their operator==() function.

template <typename T, typename U>
    void is(T got, U expected, std::string name = std::string(""));

We could rewrite our first test using is():

// equivalent to ok(5 == 5, "Five is equal to five.");
is(5, 5, "Five is equal to five.");

Running this, we get the same output as before:

1..1
ok 1 - Five is equal to five.

If we have two types that don't have an operator== function, we can specialize the cipra::equal_traits template on our types.

What about our failing test?

is(5, 6, "Five is equal to six.");

Compared to the output we got with ok(), a failing is() gives much better diagnostics. You need to provide an operator<<() function for both types so it can be outputted or specialize the cipra::print_traits template.

1..2
ok 1 - Five is equal to five.
not ok 1 - Five is equal to six.
#         got: 5
#    expected: 6

Similarly, there is an isnt() function template which checks whether the two values are not equal.

Testing Exceptions

In addition to simple truth values, cipra can also test exceptions from a function. Normally, exceptions cause the test functions we've seen to fail in the same way that a false value does. The exceptions are not propagated outside the test function, so we can't easily check whether the test function failed because of an exception or because of a false return value. If we need to, we can wrap our lambda code in a try-catch block. Let's say the function double div(double, double) throws an exception when its second argument is 0.0. If we want to check that this correctly throws an exception, we might write the following test:

ok([](){
       try {
           div(1.0, 0.0);
           return false; // this should never execute!
       } catch (...) {
           return true;
       }
   }, "div() throws an exception for a 0.0 denominator.");

Though this code does exactly what we want it to, the actual test is very verbose, because the ok() function doesn't semantically encode the exception test. Luckily, though, cipra provides the function templates throws() that do:

template<typename funcT>
    void throws(funcT expr, std::string name = std::string{""});
template<typename exceptionT, typename funcT>
    void throws(funcT expr, std::string name = std::string{""});

The first overload just checks that the expr throws some exception, while the second overload checks that expr throws an exception of the type funcT. In our case, we can simplify our code with the first overload:

throws([](){ div(1.0, 0.0); },
       "div() throws an exception for a 0.0 denominator.");

If we want to test that it is of the type std::runtime_error (or a derived class from this type), we can use the second overload:

throws<std::runtime_error>([](){ div(1.0, 0.0); },
    "div() throws an exception for a 0.0 denominator.");

This overload succeeds only when a std::runtime_error is thrown. If any other type of exception is thrown, it will catch it and fail, printing diagnostic information. If no exception is thrown, the test function will also fail.

On compilers that support the CXA ABI (for instance, GCC), you can define the preprocessor symbol CIPRA_CXA_ABI before including cipra.h. cipra will then use information available from the abi::__cxa_current_exception_type() function to determine the name of the exception type and will output it with the diagnostics. Otherwise, the name of the exception won't be available.

cipra also has two function templates nothrows() which do the inverse of the throws() templates.

Testing Constructors

Take the following class:

// Holds a floating point value with an associated experimental error.
// Invariant: The error is never negative.
template <typename T> class value {
    T val, err;
public:
    value(T val, T err = 0) : val(val), err(err) {
        if (error < 0)
            throw std::runtime_error("Cannot have negative error.");
    }
    value (const value<T>& v) : val(v.val), err(v.err) {
    }
    value operator+(const value<T> rhs) const {
        return {val + rhs.val, std::sqrt(err*err + rhs.err*rhs.err)};
    }
    bool operator==(const value<T> rhs) const {
        constexpr static epsilon = 0.0001;
        return std::abs(val - rhs.val) < epsilon &&
               std::abs(err - rhs.err) < epsilon;
    }
    /* etc */
};

If we want to test the constructor, we could write the following code:

throws([](){ value<double> v{1.0, -0.5}; },
       "Can't have negative error.");
nothrows([](){ value<double> w{1.0, 0.5}; },
         "Positive errors are fine.");
is(w, value<double>{0.5, 0.0} + value<double>{0.5, 0.5},
   "Addition operator works fine.");

v's constructor will fail and throw an exception, which causes the first test function to succeed. w's constructor will succeed, and w will be properly constructed, so the second test function will also succeed. Because w is inside a lambda, though, we can't use it in subsequent code. The third test statement will fail to compile, because w isn't in scope. We could write the following code to reconstruct it in the outer scope:

throws([](){ value<double> v{1.0, -0.5}; },
       "Can't have negative error.");
nothrows([](){ value<double> w{1.0, 0.5}; },
         "Positive errors are fine.");
value<double> w{1.0, 0.5};
is(w, value<double>{0.5, 0.0} + value<double>{0.5, 0.5},
   "Addition operator works fine.");

However, if w failed to construct the first time, it will fail to construct again, and we'll have leaked an exception.

cipra provides a function template specifically to test constructors. The function template, called new_ok, has the following signature:

template<typename T, typename... argsT> T new_ok(argsT&&... args);

new_ok constructs a object of type T using T's constructor that takes the given args. If the constructor of the object fails (that is, if the constructor throws an exception), new_ok will abort from the test program. This function requires that T either be Copyable or Movable. Since the value<double> class in our example is Copyable, we can simplify our test snippet:

throws([](){ value<double> v{1.0, -0.5}; },
       "Can't have negative error.");
auto w = new_ok<value<double>>(1.0, 0.5);
is(w, value<double>{0.5, 0.0} + value<double>{0.5, 0.5},
   "Addition operator works fine.");

w is in the outer scope now, and it can be used in subsequent test calls, like the is() call. If the constructor were to fail, the is() would never be called, and the test would exit.

File Copyright and License

Copyright (C) 2013, Patrick M. Niedzielski

This work is licensed under a Creative Commons Attribution 3.0 United States License. You should have received a copy of the CC-BY 3.0 US license along with this file. If not, see the Creative Commons website.