-# Testing
-
-* * * * *
-
-**Based on materials by Katy Huff, Rachel Slaybaugh, and Anthony
-Scopatz**
-
-
-
-# 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](http://www.ima.umn.edu/~arnold/disasters/patriot.html))
-- 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](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:
-
-```python
-def mean(numlist):
- total = sum(numlist)
- length = len(numlist)
- return total/length
-```
-
-Tests could be implemented as runtime **exceptions in the function**:
-
-```python
-def mean(numlist):
- try:
- total = sum(numlist)
- length = len(numlist)
- except TypeError:
- raise TypeError("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.
-
-```python
-def mean(numlist):
- try:
- total = sum(numlist)
- length = len(numlist)
- except TypeError:
- raise TypeError("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.
-
-```python
-def mean(numlist):
- try:
- total = sum(numlist)
- length = len(numlist)
- except TypeError:
- raise TypeError("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:
-
-```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:
-
-```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).
-
-## 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:
-
-```python
-from nose.tools 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:
-
-```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:
-
-```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:
-
-```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!
-
-```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...
-
-```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.
-
-```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:
-
-```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)
-```
-
-# 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!
-
-# 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.](http://inscight.org/2012/03/31/evolution_of_a_solution/) 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.
-
-
-> -
-
-```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]])
-```
-
projects / swc-testing-nose.git / commitdiff
