Boost.Test > Tutorials and usage recommendations > Introduction into testing

# Introduction into testing

For almost everyone, the first introduction to the craft of programming is a version of the simple "Hello World" program. In C++, this first example might be written as

    #include <ostream>

    int main()
    {
        std::cout << "Hello World\n";
    }

This is a good introduction for several reasons. One is that the program is short enough, and the logic of its execution simple enough that direct inspection can show whether it is correct in all use cases known to the new student programmer. If this were the complexity of all programming, there would be no need to test anything before using it. In programming as a new student experiences it, testing is pointless and adds unneeded complexity.

However, no actual programs are as simple as an introductory lesson makes "Hello World" seem. Not even "Hello World". In all real programs, there are decisions to be made and multiple paths of execution based on these decisions. These decisions could be based on user input, streaming data, resource availability and dozens of other factors. The programmer strives to control the inputs, and results of these decisions, but no one can keep all of them clearly in mind once the size of the project exceeds just a few hundred lines. Even "Hello World" hides complexities of this sort in the simple seeming call to std::cout.

Since the individual programmer can no longer determine the correctness of the program, there is a need for a different approach. An obvious possibility is testing the program after construction. Someone develops a set of test cases, where inputs are given to the program such that the behavior and outputs of a correctly performing program are known. The performance of the new program is compared to known standards and the new program either passes or fails. If it fails, attempts are made to fix it. If the test cases are carefully chosen, the specifics of the failure give an indication of what in the program needs to be fixed.

This is an improvement over just not knowing whether the program is working properly, but it isn't a big improvement. If the whole program is tested at once, it is nearly impossible to develop test cases that clearly indicate what the failure is. The system is too complex, and the programmer still needs to understand almost all of the possible outcomes to be able to develop tests. As always, when a problem is too big and complicated a good idea is to try splitting it into smaller and simpler pieces.

This approach leads to a layered system of testing, that is similar to the layered approach to original development and should be integrated into it. When writing a program, the design is factored into small units that are conceptually and structurally easier to grasp. A standard rule for this is that one unit performs one job or embodies one concept. These simple units are composed into larger and more complicated algorithms by passing needed information into a unit and receiving the desired result out of it. The units are integrated to perform the whole task. Testing should reflect this structure of development.

The simplest layer is Unit Testing. A unit is the smallest conceptually whole segment of the program. Examples of basic units might be a single class or a single function. For each unit, the tester (who may or may not be the programmer) attempts to determine what states the unit can encounter while executing as part of the program. These states include determining the range of appropriate inputs to the unit, determining the range of possible inappropriate inputs, and recognizing any ways the state of the rest of the program might affect execution in this unit.

With so many general statements, an example will help clarify. Imagine the following procedural function is part of a program, and the programmer wants to test it. For the sake of brevity, header includes and namespace qualifiers have been suppressed.

    double find_root( double             (*f)(double),
                      double               low_guess,
                      double               high_guess,
                      std::vector<double>& steps,
                      double               tolerance )
    {
        double solution;
        bool   converged = false;

        while(not converged)
        {
            double temp = (low_guess + high_guess) / 2.0;
            steps.push_back( temp );

            double f_temp = f(temp);
            double f_low = f(low_guess);

            if(abs(f_temp) < tolerance)
            {
                solution  = temp;
                converged = true;
            }
            else if(f_temp / abs(f_temp) == f_low / abs(f_low))
            {
                low_guess = temp;
                converged = false;
            }
            else
            {
                high_guess = temp;
                converged = false;
            }
        }

        return solution;
    }

This code, although brief and simple is getting long enough that it takes attention to find what is done and why. It is no longer obvious at a glance what the intent of the program is, so careful naming must be used to carry that intent.

Thanks to the control structures, there are some obvious execution paths in the code. However, there are also a few less obvious paths. For example, if the root finder takes many steps to converge to an acceptable answer, the vector that is holding the history of steps taken may need to reallocate for additional space. In this case, there are many hidden steps in the single push_back command. These steps also include the chance of failure, since that is always a possibility in a memory allocation.

A second example notes that the value of the function at the low guess has not been tested, so there is the chance of a zero division. Also, if the value of the function at the high guess is zero, the root finder will miss that root entirely. It may even fall into an infinite loop if no root lies between the low and high values.

In this unit, proper testing includes checking the behavior in each possibility. It also includes checking the function by giving inputs where the correct answer is known and checking the results against that answer. Thus, the unit is tested in every execution path to assure proper behavior.

Test cases are chosen to expose as many errors as possible. A defining characteristic of a good test case is that the programmer knows what the unit should do if it is functioning properly. Test cases should be generated to exercise each available execution path. For the above snippet, this includes the obvious and the not so obvious paths. Every path should be tested, since every path is a possible outcome of program execution.

Thus, to write a good testing suite, the tester must know the structure of the code. The most dependable way to accomplish this is if the original programmer writes tests as part of creating the code. In fact, it is advisable that the tests are produced before the code is written, and updated whenever structure decisions are changed. This way, the tests are written with a view toward how the unit should perform instead of reproducing the programmer's thinking from writing the code. While black box testing is also useful, it is important that someone who knows the design decisions made and the rationale for those decisions test the code unit. A programmer who can't devise good tests for a unit does not yet know the problem at hand well enough to program dependably.

When a unit is completed and tested, it is ready for integration with other units in the program. This is integration should also be tested. At this point, the test cases focus on the interaction between the units. Tests are designed to exercise each way the units can affect each other.

This is the point in development where proper unit testing really shines. If each unit is doing what it should be doing and not creating unexpected side effects, any issues in testing a set of integrated units must come from how they are passing information. Thus, the nearly intractable problem of finding an error while many units interact becomes the less intimidating problem of finding the breakdown in communications.

At each layer of increasing complexity, new tests are run, and if the prior tests of the components are well designed and all issues are fixed, new errors are isolated to the integration. This process continues, in parallel with development, from the smallest units to the completed program.

This shows that there is a need to be able to check and test code snippets such as individual functions and classes independent of the program of which they will become a part. That is, the need for a means to provide predetermined inputs to the unit and to check the outputs against expected results. Such a system must allow for both normal operation and error conditions, and allow the programmer to produce a thorough description of the results.

This is the goal and rationale for all unit testing, and supporting testing of this sort is the purpose of the Boost.Test library. As is shown below, Boost.Test provides a well-integrated set of tools to support this testing effort throughout the programming and maintenance cycles of software development.