--- /dev/null
+# Testing
+
+![image](media/test-in-production.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? ([Patriot Missile Failure][patriot])
+- 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's the limit on that input parameter?
+- What about **edge or corner cases**?
+- How will it affect your [publications][]?
+
+## 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.
+
+
+[patriot]: http://www.ima.umn.edu/~arnold/disasters/patriot.html
+[publications]: http://www.nature.com/news/2010/101013/full/467775a.html
--- /dev/null
+# Mean-calculation example
+
+* Basic implementation: [mean.py][basic-mean]
+* Internal exception catching: [mean.py][exception-mean]
+* Embedded tests: [mean.py][embedded-test-mean]
+* Independent tests: [test_mean.py][test-mean]
+
+# When should we test?
+
+Short answers:
+
+- **ALWAYS!**
+- **EARLY!**
+- **OFTEN!**
+
+Long answers:
+
+* Definitely before you do something important with your software
+ (e.g. publishing data generated by your program, launching a
+ satellite that depends on your software, …).
+* Before and after adding something new, to avoid accidental breakage.
+* To help remember ([TDD][]: define) what your code actually does.
+
+# Who should test?
+
+* Write tests for the stuff you code, to convince your collaborators
+ that it works.
+* Write tests for the stuff others code, to convince yourself that it
+ works (and will continue to work).
+
+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:
+
+```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:
+
+```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](http://www.junit.org/) in Java which can
+arguably attributed to inventing the testing framework.
+
+## 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.
+
+```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.
+
+```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:
+
+```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 first**. 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](http://www.amazon.com/Test-Driven-Development-By-Example/dp/0321146530).
+
+For an example of TDD, see [the Fibonacci example][fibonacci].
+
+# Quality Assurance Exercise
+
+Can you think of other tests to make for the fibonacci function? I promise there
+are at least two.
+
+Implement one new test in test_fib.py, run nosetests, and if it fails, implement
+a more robust function for that case.
+
+And thus - finally - we have a robust function together with working
+tests!
+
+
+[basic-mean]: exercises/mean/basic/mean.py
+[exception-mean]: exercises/mean/exceptions/mean.py
+[embedded-test-mean]: exercises/embedded-tests/mean.py
+[test-mean]: exercises/test_mean.py
+[TDD]: http://en.wikipedia.org/wiki/Test-driven_development
+[fibonacci]: exercises/fibonacci