# Unit testing

## Introduction

Many aspects in the design flow of modern digital hardware design can be viewed as a special kind of software development. From that viewpoint, it is a natural question whether advances in software design techniques can not also be applied to hardware design.

One software design approach that deserves attention is Extreme Programming (XP). It is a fascinating set of techniques and guidelines that often seems to go against the conventional wisdom. On other occasions, XP just seems to emphasize the common sense, which doesn’t always coincide with common practice. For example, XP stresses the importance of normal workweeks, if we are to have the fresh mind needed for good software development.

It is not my intention nor qualification to present a tutorial on Extreme Programming. Instead, in this section I will highlight one XP concept which I think is very relevant to hardware design: the importance and methodology of unit testing.

## The importance of unit tests

Unit testing is one of the corner stones of Extreme Programming. Other XP concepts, such as collective ownership of code and continuous refinement, are only possible by having unit tests. Moreover, XP emphasizes that writing unit tests should be automated, that they should test everything in every class, and that they should run perfectly all the time.

I believe that these concepts apply directly to hardware design. In addition, unit tests are a way to manage simulation time. For example, a state machine that runs very slowly on infrequent events may be impossible to verify at the system level, even on the fastest simulator. On the other hand, it may be easy to verify it exhaustively in a unit test, even on the slowest simulator.

It is clear that unit tests have compelling advantages. On the other hand, if we need to test everything, we have to write lots of unit tests. So it should be easy and pleasant to create, manage and run them. Therefore, XP emphasizes the need for a unit test framework that supports these tasks. In this chapter, we will explore the use of the `unittest` module from the standard Python library for creating unit tests for hardware designs.

## Unit test development

In this section, we will informally explore the application of unit test techniques to hardware design. We will do so by a (small) example: testing a binary to Gray encoder as introduced in section Bit indexing.

### Defining the requirements

We start by defining the requirements. For a Gray encoder, we want to the output to comply with Gray code characteristics. Let’s define a code as a list of codewords, where a codeword is a bit string. A code of order `n` has `2**n` codewords.

A well-known characteristic is the one that Gray codes are all about:

Consecutive codewords in a Gray code should differ in a single bit.

Is this sufficient? Not quite: suppose for example that an implementation returns the lsb of each binary input. This would comply with the requirement, but is obviously not what we want. Also, we don’t want the bit width of Gray codewords to exceed the bit width of the binary codewords.

Each codeword in a Gray code of order n must occur exactly once in the binary code of the same order.

With the requirements written down we can proceed.

### Writing the test first

A fascinating guideline in the XP world is to write the unit test first. That is, before implementing something, first write the test that will verify it. This seems to go against our natural inclination, and certainly against common practices. Many engineers like to implement first and think about verification afterwards.

But if you think about it, it makes a lot of sense to deal with verification first. Verification is about the requirements only — so your thoughts are not yet cluttered with implementation details. The unit tests are an executable description of the requirements, so they will be better understood and it will be very clear what needs to be done. Consequently, the implementation should go smoother. Perhaps most importantly, the test is available when you are done implementing, and can be run anytime by anybody to verify changes.

Python has a standard `unittest` module that facilitates writing, managing and running unit tests. With `unittest`, a test case is written by creating a class that inherits from `unittest.TestCase`. Individual tests are created by methods of that class: all method names that start with `test` are considered to be tests of the test case.

We will define a test case for the Gray code properties, and then write a test for each of the requirements. The outline of the test case class is as follows:

    import unittest

    class TestGrayCodeProperties(unittest.TestCase):

        def testSingleBitChange(self):
         """Check that only one bit changes in successive codewords."""
         ....

        def testUniqueCodeWords(self):
        """Check that all codewords occur exactly once."""
        ....

Each method will be a small test bench that tests a single requirement. To write the tests, we don’t need an implementation of the Gray encoder, but we do need the interface of the design. We can specify this by a dummy implementation, as follows:

For the first requirement, we will test all consecutive input numbers, and compare the current output with the previous one For each input, we check that the difference is exactly a single bit. For the second requirement, we will test all input numbers and put the result in a list. The requirement implies that if we sort the result list, we should get a range of numbers. For both requirements, we will test all Gray codes up to a certain order `MAX_WIDTH`. The test code looks as follows:

Note how the actual check is performed by a `self.assertEqual` method, defined by the `unittest.TestCase` class. Also, we have factored out running the tests for all Gray codes in a separate method `runTests`.

### Test-driven implementation

With the test written, we begin with the implementation. For illustration purposes, we will intentionally write some incorrect implementations to see how the test behaves.

The easiest way to run tests defined with the `unittest` framework, is to put a call to its `main` method at the end of the test module:

    unittest.main()

Let’s run the test using the dummy Gray encoder shown earlier:

    % python test_gray_properties.py
    testSingleBitChange (__main__.TestGrayCodeProperties)
    Check that only one bit changes in successive codewords. ... ERROR
    testUniqueCodeWords (__main__.TestGrayCodeProperties)
    Check that all codewords occur exactly once. ... ERROR

As expected, this fails completely. Let us try an incorrect implementation, that puts the lsb of in the input on the output:

Running the test produces:

    python test_gray_properties.py
    testSingleBitChange (__main__.TestGrayCodeProperties)
    Check that only one bit changes in successive codewords. ... ok
    testUniqueCodeWords (__main__.TestGrayCodeProperties)
    Check that all codewords occur exactly once. ... FAIL

    ======================================================================
    FAIL: testUniqueCodeWords (__main__.TestGrayCodeProperties)
    Check that all codewords occur exactly once.
    ----------------------------------------------------------------------
    Traceback (most recent call last):
      File "test_gray_properties.py", line 42, in testUniqueCodeWords
        self.runTests(test)
      File "test_gray_properties.py", line 53, in runTests
        sim.run(quiet=1)
      File "/home/jand/dev/myhdl/myhdl/_Simulation.py", line 154, in run
        waiter.next(waiters, actives, exc)
      File "/home/jand/dev/myhdl/myhdl/_Waiter.py", line 127, in next
        clause = next(self.generator)
      File "test_gray_properties.py", line 40, in test
        self.assertEqual(actual, expected)
    AssertionError: Lists differ: [0, 0, 1, 1] != [0, 1, 2, 3]

    First differing element 1:
    0
    1

    - [0, 0, 1, 1]
    + [0, 1, 2, 3]

    ----------------------------------------------------------------------
    Ran 2 tests in 0.083s

    FAILED (failures=1)

Now the test passes the first requirement, as expected, but fails the second one. After the test feedback, a full traceback is shown that can help to debug the test output.

Finally, we use a correct implementation:

Now the tests pass:

### Additional requirements

In the previous section, we concentrated on the general requirements of a Gray code. It is possible to specify these without specifying the actual code. It is easy to see that there are several codes that satisfy these requirements. In good XP style, we only tested the requirements and nothing more.

It may be that more control is needed. For example, the requirement may be for a particular code, instead of compliance with general properties. As an illustration, we will show how to test for the original Gray code, which is one specific instance that satisfies the requirements of the previous section. In this particular case, this test will actually be easier than the previous one.

We denote the original Gray code of order `n` as `Ln`. Some examples:

    L1 = ['0', '1']
    L2 = ['00', '01', '11', '10']
    L3 = ['000', '001', '011', '010', '110', '111', '101', 100']

It is possible to specify these codes by a recursive algorithm, as follows:

1. L1 = [‘0’, ‘1’]
2. Ln+1 can be obtained from Ln as follows. Create a new code Ln0 by prefixing all codewords of Ln with ‘0’. Create another new code Ln1 by prefixing all codewords of Ln with ‘1’, and reversing their order. Ln+1 is the concatenation of Ln0 and Ln1.

Python is well-known for its elegant algorithmic descriptions, and this is a good example. We can write the algorithm in Python as follows:

The code `['0' + codeword for ...]` is called a list comprehension. It is a concise way to describe lists built by short computations in a for loop.

The requirement is now that the output code matches the expected code Ln. We use the `nextLn` function to compute the expected result. The new test case code is as follows:

As it happens, our implementation is apparently an original Gray code: