-`Back To Debugging`_ - `Forward To Documentation`_
-
-.. _Back To Debugging: https://github.com/thehackerwithin/UofCSCBC2012/tree/master/4-Debugging/
-.. _Forward To Documentation: https://github.com/thehackerwithin/UofCSCBC2012/tree/master/6-Documentation/
-
------------
-
-**Presented By Anthony Scopatz**
-
-**Based on materials by Katy Huff, Rachel Slaybaugh, and Anthony Scopatz**
-
-.. image:: https://github.com/thehackerwithin/UofCSCBC2012/raw/scopz/5-Testing/test_prod.jpg
-
-
-What is testing?
-================
-Software testing is a process by which one or more expected behaviors and
-results from a piece of software are exercised and confirmed. Well chosen
-tests will confirm expected code behavior for the extreme boundaries of the
-input domains, output ranges, parametric combinations, and other behavioral
-edge cases.
-
-Why test software?
-==================
-Unless you write flawless, bug-free, perfectly accurate, fully precise, and
-predictable code every time, you must test your code in order to trust it
-enough to answer in the affirmative to at least a few of the following questions:
-
-* Does your code work?
-* Always?
-* Does it do what you think it does?
-* Does it continue to work after changes are made?
-* Does it continue to work after system configurations or libraries are upgraded?
-* Does it respond properly for a full range of input parameters?
-* What about edge or corner cases?
-* What's the limit on that input parameter?
-* How will it affect your `publications`_?
-
-.. _publications: http://www.nature.com/news/2010/101013/full/467775a.html
-
-Verification
-************
-*Verification* is the process of asking, "Have we built the software correctly?"
-That is, is the code bug free, precise, accurate, and repeatable?
-
-Validation
-**********
-*Validation* is the process of asking, "Have we built the right software?"
-That is, is the code designed in such a way as to produce the answers we are
-interested in, data we want, etc.
-
-Uncertainty Quantification
-**************************
-*Uncertainty Quantification* is the process of asking, "Given that our algorithm
-may not be deterministic, was our execution within acceptable error bounds?" This
-is particularly important for anything which uses random numbers, eg Monte Carlo methods.
-
-
-Where are tests?
-================
-Say we have an averaging function:
-
-.. code-block:: python
-
- def mean(numlist):
- total = sum(numlist)
- length = len(numlist)
- return total/length
-
-Tests could be implemented as runtime exceptions in the function:
-
-.. code-block:: python
-
- def mean(numlist):
- try:
- total = sum(numlist)
- length = len(numlist)
- except ValueError:
- print "The number list was not a list of numbers."
- except:
- print "There was a problem evaluating the number list."
- return total/length
-
-
-Sometimes tests they are functions alongside the function definitions they are testing.
-
-.. code-block:: python
-
- def mean(numlist):
- try:
- total = sum(numlist)
- length = len(numlist)
- except ValueError:
- print "The number list was not a list of numbers."
- except:
- print "There was a problem evaluating the number list."
- return total/length
-
-
- def test_mean():
- assert mean([0, 0, 0, 0]) == 0
- assert mean([0, 200]) == 100
- assert mean([0, -200]) == -100
- assert mean([0]) == 0
-
-
- def test_floating_mean():
- assert mean([1, 2]) == 1.5
-
-Sometimes they are in an executable independent of the main executable.
-
-.. code-block:: python
-
- def mean(numlist):
- try:
- total = sum(numlist)
- length = len(numlist)
- except ValueError:
- print "The number list was not a list of numbers."
- except:
- print "There was a problem evaluating the number list."
- return total/length
-
-
-Where, in a different file exists a test module:
-
-.. code-block:: python
-
- import mean
-
- def test_mean():
- assert mean([0, 0, 0, 0]) == 0
- assert mean([0, 200]) == 100
- assert mean([0, -200]) == -100
- assert mean([0]) == 0
-
-
- def test_floating_mean():
- assert mean([1, 2]) == 1.5
-
-When should we test?
-====================
-The three right answers are:
-
-* **ALWAYS!**
-* **EARLY!**
-* **OFTEN!**
-
-The longer answer is that testing either before or after your software
-is written will improve your code, but testing after your program is used for
-something important is too late.
-
-If we have a robust set of tests, we can run them before adding something new and after
-adding something new. If the tests give the same results (as appropriate), we can have
-some assurance that we didn't wreak anything. The same idea applies to making changes in
-your system configuration, updating support codes, etc.
-
-Another important feature of testing is that it helps you remember what all the parts
-of your code do. If you are working on a large project over three years and you end up
-with 200 classes, it may be hard to remember what the widget class does in detail. If
-you have a test that checks all of the widget's functionality, you can look at the test
-to remember what it's supposed to do.
-
-Who should test?
-================
-In a collaborative coding environment, where many developers contribute to the same code base,
-developers should be responsible individually for testing the functions they create and
-collectively for testing the code as a whole.
-
-Professionals often test their code, and take pride in test coverage, the percent
-of their functions that they feel confident are comprehensively tested.
-
-How are tests written?
-======================
-The type of tests that are written is determined by the testing framework you adopt.
-Don't worry, there are a lot of choices.
-
-Types of Tests
-****************
-**Exceptions:** Exceptions can be thought of as type of runtime test. They alert
-the user to exceptional behavior in the code. Often, exceptions are related to
-functions that depend on input that is unknown at compile time. Checks that occur
-within the code to handle exceptional behavior that results from this type of input
-are called Exceptions.
-
-**Unit Tests:** Unit tests are a type of test which test the fundamental units of a
-program's functionality. Often, this is on the class or function level of detail.
-However what defines a *code unit* is not formally defined.
-
-To test functions and classes, the interfaces (API) - rather than the implementation - should
-be tested. Treating the implementation as a black box, we can probe the expected behavior
-with boundary cases for the inputs.
-
-**System Tests:** System level tests are intended to test the code as a whole. As opposed
-to unit tests, system tests ask for the behavior as a whole. This sort of testing involves
-comparison with other validated codes, analytical solutions, etc.
-
-**Regression Tests:** A regression test ensures that new code does change anything.
-If you change the default answer, for example, or add a new question, you'll need to
-make sure that missing entries are still found and fixed.
-
-**Integration Tests:** Integration tests query the ability of the code to integrate
-well with the system configuration and third party libraries and modules. This type
-of test is essential for codes that depend on libraries which might be updated
-independently of your code or when your code might be used by a number of users
-who may have various versions of libraries.
-
-**Test Suites:** Putting a series of unit tests into a collection of modules creates,
-a test suite. Typically the suite as a whole is executed (rather than each test individually)
-when verifying that the code base still functions after changes have been made.
-
-Elements of a Test
-==================
-**Behavior:** The behavior you want to test. For example, you might want to test the fun()
-function.
-
-**Expected Result:** This might be a single number, a range of numbers, a new fully defined
-object, a system state, an exception, etc. When we run the fun() function, we expect to
-generate some fun. If we don't generate any fun, the fun() function should fail its test.
-Alternatively, if it does create some fun, the fun() function should pass this test.
-The the expected result should known *a priori*. For numerical functions, this is
-result is ideally analytically determined even if the function being tested isn't.
-
-**Assertions:** Require that some conditional be true. If the conditional is false,
-the test fails.
-
-**Fixtures:** Sometimes you have to do some legwork to create the objects that are
-necessary to run one or many tests. These objects are called fixtures as they are not really
-part of the test themselves but rather involve getting the computer into the appropriate state.
-
-For example, since fun varies a lot between people, the fun() function is a method of
-the Person class. In order to check the fun function, then, we need to create an appropriate
-Person object on which to run fun().
-
-**Setup and teardown:** Creating fixtures is often done in a call to a setup function.
-Deleting them and other cleanup is done in a teardown function.
-
-**The Big Picture:** Putting all this together, the testing algorithm is often:
-
-.. code-block:: python
-
- setup()
- test()
- teardown()
-
-
-But, sometimes it's the case that your tests change the fixtures. If so, it's better
-for the setup() and teardown() functions to occur on either side of each test. In
-that case, the testing algorithm should be:
-
-.. code-block:: python
-
- setup()
- test1()
- teardown()
-
- setup()
- test2()
- teardown()
-
- setup()
- test3()
- teardown()
-
-----------------------------------------------------------
-
-Nose: A Python Testing Framework
-================================
-The testing framework we'll discuss today is called nose. However, there are several
-other testing frameworks available in most language. Most notably there is `JUnit`_
-in Java which can arguably attributed to inventing the testing framework.
-
-.. _nose: http://readthedocs.org/docs/nose/en/latest/
-.. _JUnit: http://www.junit.org/
-
-Where do nose tests live?
-*************************
-Nose tests are files that begin with ``Test-``, ``Test_``, ``test-``, or ``test_``.
-Specifically, these satisfy the testMatch regular expression ``[Tt]est[-_]``.
-(You can also teach nose to find tests by declaring them in the unittest.TestCase
-subclasses chat you create in your code. You can also create test functions which
-are not unittest.TestCase subclasses if they are named with the configured
-testMatch regular expression.)
-
-Nose Test Syntax
-****************
-To write a nose test, we make assertions.
-
-.. code-block:: python
-
- assert should_be_true()
- assert not should_not_be_true()
-
-Additionally, nose itself defines number of assert functions which can be used to
-test more specific aspects of the code base.
-
-.. code-block:: python
-
- from nose.tools import *
-
- assert_equal(a, b)
- assert_almost_equal(a, b)
- assert_true(a)
- assert_false(a)
- assert_raises(exception, func, *args, **kwargs)
- assert_is_instance(a, b)
- # and many more!
-
-Moreover, numpy offers similar testing functions for arrays:
-
-.. code-block:: python
-
- from numpy.testing import *
-
- assert_array_equal(a, b)
- assert_array_almost_equal(a, b)
- # etc.
-
-Exercise: Writing tests for mean()
-**********************************
-There are a few tests for the mean() function that we listed in this lesson.
-What are some tests that should fail? Add at least three test cases to this set.
-Edit the ``test_mean.py`` file which tests the mean() function in ``mean.py``.
-
-*Hint:* Think about what form your input could take and what you should do to handle it.
-Also, think about the type of the elements in the list. What should be done if you pass
-a list of integers? What if you pass a list of strings?
-
-**Example**::
-
- nosetests test_mean.py
-
-Test Driven Development
-=======================
-Test driven development (TDD) is a philosophy whereby the developer creates code by
-**writing the tests fist**. That is to say you write the tests *before* writing the
-associated code!
-
-This is an iterative process whereby you write a test then write the minimum amount
-code to make the test pass. If a new feature is needed, another test is written and
-the code is expanded to meet this new use case. This continues until the code does
-what is needed.
-
-TDD operates on the YAGNI principle (You Ain't Gonna Need It). People who diligently
-follow TDD swear by its effectiveness. This development style was put forth most
-strongly by `Kent Beck in 2002`_.
-
-.. _Kent Beck in 2002: http://www.amazon.com/Test-Driven-Development-By-Example/dp/0321146530
-
-A TDD Example
-*************
-Say you want to write a fib() function which generates values of the
-Fibonacci sequence of given indexes. You would - of course - start
-by writing the test, possibly testing a single value:
-
-.. code-block:: python
-
- from nose import assert_equal
-
- from pisa import fib
-
- def test_fib1():
- obs = fib(2)
- exp = 1
- assert_equal(obs, exp)
-
-You would *then* go ahead and write the actual function:
-
-.. code-block:: python
-
- def fib(n):
- # you snarky so-and-so
- return 1
-
-And that is it right?! Well, not quite. This implementation fails for
-most other values. Adding tests we see that:
-
-.. code-block:: python
-
- def test_fib1():
- obs = fib(2)
- exp = 1
- assert_equal(obs, exp)
-
-
- def test_fib2():
- obs = fib(0)
- exp = 0
- assert_equal(obs, exp)
-
- obs = fib(1)
- exp = 1
- assert_equal(obs, exp)
-
-This extra test now requires that we bother to implement at least the initial values:
-
-.. code-block:: python
-
- def fib(n):
- # a little better
- if n == 0 or n == 1:
- return n
- return 1
-
-However, this function still falls over for ``2 < n``. Time for more tests!
-
-.. code-block:: python
-
- def test_fib1():
- obs = fib(2)
- exp = 1
- assert_equal(obs, exp)
-
-
- def test_fib2():
- obs = fib(0)
- exp = 0
- assert_equal(obs, exp)
-
- obs = fib(1)
- exp = 1
- assert_equal(obs, exp)
-
-
- def test_fib3():
- obs = fib(3)
- exp = 2
- assert_equal(obs, exp)
-
- obs = fib(6)
- exp = 8
- assert_equal(obs, exp)
-
-At this point, we had better go ahead and try do the right thing...
-
-.. code-block:: python
-
- def fib(n):
- # finally, some math
- if n == 0 or n == 1:
- return n
- else:
- return fib(n - 1) + fib(n - 2)
-
-Here it becomes very tempting to take an extended coffee break or possibly a
-power lunch. But then you remember those pesky negative numbers and floats.
-Perhaps the right thing to do here is to just be undefined.
-
-.. code-block:: python
-
- def test_fib1():
- obs = fib(2)
- exp = 1
- assert_equal(obs, exp)
-
-
- def test_fib2():
- obs = fib(0)
- exp = 0
- assert_equal(obs, exp)
-
- obs = fib(1)
- exp = 1
- assert_equal(obs, exp)
-
-
- def test_fib3():
- obs = fib(3)
- exp = 2
- assert_equal(obs, exp)
-
- obs = fib(6)
- exp = 8
- assert_equal(obs, exp)
-
-
- def test_fib3():
- obs = fib(13.37)
- exp = NotImplemented
- assert_equal(obs, exp)
-
- obs = fib(-9000)
- exp = NotImplemented
- assert_equal(obs, exp)
-
-This means that it is time to add the appropriate case to the function itself:
-
-.. code-block:: python
-
- def fib(n):
- # sequence and you shall find
- if n < 0 or int(n) != n:
- return NotImplemented
- elif n == 0 or n == 1:
- return n
- else:
- return fib(n - 1) + fib(n - 2)
-
-And thus - finally - we have a robust function together with working tests!
-
-Exercise
-========
-**The Problem:** In 2D or 3D, we have two points (p1 and p2) which define a line segment.
-Additionally there exists experimental data which can be anywhere in the domain.
-Find the data point which is closest to the line segment.
-
-In the ``close_line.py`` file there are four different implementations which all
-solve this problem. `You can read more about them here.`_ However, there are no tests!
-Please write from scratch a ``test_close_line.py`` file which tests the closest_data_to_line()
-functions.
-
-*Hint:* you can use one implementation function to test another. Below is some sample data
-to help you get started.
-
-.. image:: https://github.com/thehackerwithin/UofCSCBC2012/raw/scopz/5-Testing/evo_sol1.png
-
- -
-
-.. code-block:: python
-
- import numpy as np
-
- p1 = np.array([0.0, 0.0])
- p2 = np.array([1.0, 1.0])
- data = np.array([[0.3, 0.6], [0.25, 0.5], [1.0, 0.75]])
-
-.. _You can read more about them here.: http://inscight.org/2012/03/31/evolution_of_a_solution/
-
-
projects / swc-testing-nose.git / commitdiff