Zope Unit Testing

 Zope Testing

  If you encounter a directory named "tests" in a package within
  within the Zope source code, it most likely indicates that the
  directory contains test code used to ensure that the code owned by
  the package works as it was designed.  Many of the test scripts
  contained within Zope "tests" directories will be scripts which
  contain "unit tests".

 What Unit Tests Are

  A "unit" may be defined as a piece of code with a single intended
  purpose.  A "unit test" is defined as a piece of code which exists
  to codify the intended behavior of a unit and to compare its
  intended behavior against its actual behavior.

  Unit tests are a way for developers and quality assurance engineers
  to quickly ascertain whether independent units of code are working as
  expected.  Unit tests are generally written at the same time as the
  code they are intended to test.  A unit testing framework allows a
  collection of unit tests to be run without human intervention,
  producing a minimum of output if all the tests in the collection are
  successful.

 What Unit Tests Are Not

  It's very useful to define unit tests in terms of what they are
  not.  From the "Extreme Programming Enthusiast" website
  (http://c2.com/cgi/wiki?UnitTestsDefined)::

   Unit tests are not:

    - Manually operated.

    - Automated screen-driver tests that simulate user input (these
      are "functional tests").

    - Interactive.  They run "no questions asked."

    - Coupled.  They run without dependencies except those native to
      the thing being tested.

    - Complicated.  Unit test code is typically straightforward
      procedural code that simulates an event.

 Unit Testing Frameworks

  A unit testing framework is generally employed to collect related
  unit tests together in order to make it easier to run them as a
  group.  When used with a unit testing framework, unit tests live
  outside of the modules of code they're intended to test.

 How Unit Tests Help In The Development Process

  Unit tests should be written at the same time the code they test is
  written.  A short, healthy cycle of "code/write test/run
  test/repeat" can help a developer code more quickly by reducing
  "backtracking" effort and by helping the developer focus on the
  actual problem at hand.  Additonally, the unit tests generated at
  initial development time can serve as later assurance that
  maintenance and refactoring performed on code does not break any of
  its intended functionality or behavior.  The results of unit tests
  may additionally be used as a metric by quality assurance personnel
  along with the results of other tests to gauge code quality before
  before it is "shipped."

 Basic Unit Testing Philosophies

  Write unit tests at the same time that you write the code.
  Nothing's worse than being faced with the prospect of writing tests
  against a huge chunk of existing code because you "have to."
  Writing unit tests post-facto can be boring and also robs you of the
  main benefits that unit testing can provide.  Writing unit tests at
  the same time you write the code helps you focus on the task at
  hand.  Writing unit tests in conjuction with code can be fun and
  satisfying, and can help you improve the quality of your code while
  its goals are fresh in your mind.  Used properly, unit testing may
  also help you write code faster, because you will need to
  "backtrack" less.  Some "Extreme Programming" enthusiasts posit that
  you should write a test before you write its associated code, and
  then develop the code until it passes the test.

  Unit tests should be developed against as small and specific a
  subset of a system's or subsystem's functionality as possible.  For
  instance, a one unit test may test that a unique id generator
  produces ids of a specific length, while another unit test in the
  same group may ensure that a thousand ids from the same unique id
  generator do not contain the same value.  Writing a single unit test
  which tests a broad swath of a system's capabilities is
  counterproductive, because it does not allow for a fine enough
  granularity when attempting to figure out "what went wrong,"
  requiring the developer to "backtrack".  Unit testing is capable of
  helping to help reduce backtracking, but only if used properly.

  A unit test does not produce any output unless it fails.  If a unit
  test fails, it should print something useful, but short.  A unit
  test should never fill the screen with output or otherwise produce
  output that needs to be manually examined for "clues".  This is the
  realm of other testing methodologies.  If unit tests are written at
  sufficiently granular level, it is often enough just to know the
  name of the unit test that failed.

  "It is better to have tried to test and failed than to not have
  tried to test at all" aka "test the riskist things first."  If the
  prospect of writing a series of unit tests for an existing system
  seems daunting, it's important to remember that no matter how many
  unit tests you write, you cannot prove that your software does not
  have bugs.  Therefore, you cannot possibly test every case while
  developing.  You should plan to write tests against code based on
  the risks involved in not testing that code.  Don't feel that you
  need to write a test case for every "corner case" (although do try
  to hit the riskiest ones).  In the worst case, it's better to have a
  test module with one lonely unit test in it than none at all.

  "Test fixtures" are employed by unit tests.  Test fixtures are bits
  of state and environment that allow the unit test to perform its job
  properly.  An example of a test fixture might be a file, an instance
  of a class, or a row in a database table.  Any part of the
  environment needed by a unit test besides the unit testing framework
  itself is considered a test fixture.

  In general, the common fixtures required by individual tests in a
  testing group should be more or less identical.  If the fixtures
  needed by a single test are radically different than the fixtures
  required by the rest of the tests, or if the test does not require
  the fixtures provided to the other tests, it probably belongs in
  another (or its own) group of tests.

  When a unit test in a group modifies the state of a test fixture,
  the test fixture should be restored to a known state before the next
  unit test in the group is run.  Often, this means "rolling back"
  changes to a transactional database or restoring the state of a
  string so the next unit test can inherit a known state of the same
  fixtures.  The unit testing framework has capabilities which allow
  you to automate most of this work by providing a "set up" method
  which gets called before each test is run and a "tear down" method
  that gets called after a test is finished.

  Unit tests should play nicely with the unit testing framework they
  employ.  Unit tests should not call "sys.exit()" or do similar
  things which effect their ability to be run as part of a group of
  tests.  The testing framework attempts to deal with misbehaved unit
  tests, but it's better just to make them behave nicely in the first
  place.

  Unit tests should "grow" with the code that they're intended to
  test.  For example, if a group of unit tests is intended to verify
  the behavior of all of the routines within a module, additional unit
  tests should be added to the test group when new functionality is
  added to that module.

 Writing Unit Tests For The Zope Core

  If you're writing core code, you probably don't need to listen to
  any more of this.  :-) The rules for writing tests for Zope core
  code are simple:

   - The testing code should make use of PyUnit
     (/lib/python/unittest.py).  Instructions for using PyUnit are
     available at http://pyunit.sourceforge.net.

   - Tests must be placed in a "tests" subdirectory of the package or
     directory in which the core code you're testing lives.

   - Test modules should be named something which represents the
     functionality they test, and should begin with the prefix "test."
     E.g., a test module for BTree should be named testBTree.py.

   - An individual test module should take no longer than 60 seconds
     to complete.

 Writing Unit Tests For Applications Based On Zope

  Zope uses the PyUnit unit testing framework, the documentation for
  which is available at http://pyunit.sourceforge.net.  The
  lib/python/unittest.py module is the framework.  You should
  establish your own conventions for naming and placement of test
  modules.

  Writing unit tests against applications based on Zope can be
  difficult.  Zope is a collection of related modules, some with
  non-trivial interdependencies.  Running its code successfully also
  in some cases depends on state provided only in the context of a web
  request, so calling Zope methods directly may not work as you
  expect.  If you're not intimately familiar with Zope, implementing
  unit tests can be fustrating.  For example, for the common case,
  before you are able to write code which tests a Zope SQL Method, you
  must establish a test fixture which represents your entire Zope
  site.

  Luckily, some tools are at your disposal to make writing unit tests
  against Zope components and applications easier by making the
  creation of these fixtures easier.

  Surprisingly, one of the most effective tools for facilitating unit
  testing is ZEO (http://www.zope.org/Products/ZEO).  ZEO is an
  open-source clustering solution for Zope which makes it possible to
  front-end a single "storage server" which manages a Zope object
  database with multiple Zope clients that run a "client storage".
  The reason ZEO is interesting for unit testing is mostly an
  unintended side-effect of how it works as compared to Zope without
  ZEO.  Zope without ZEO commonly uses a "FileStorage" to hold its
  object database.  When Zope is started with a FileStorage, the
  FileStorage code processes an "index" file.  This takes time.  Zope
  using a ClientStorage as with ZEO does not process an index file,
  making startup faster.  Fast startup of Zope is critical to
  effective unit testing.  It is recommended that you implement ZEO if
  you're heavy in to unit testing, as it really speeds things up.
  It's not strictly required, however.

  Making Zope itself into a test fixture is straightforward.  Your
  test code must:

   - add the 'lib/python' directory of your Zope installation to the
     PYTHONPATH (via sys.path.insert())

   - 'import ZODB' (may not be required, but just put it in for good measure)

   - 'import Zope'

   - 'app = Zope.app()'

   - operate on the Zope instance space by calling methods from the
     root object (bound to 'app'), e.g.:

     app.acl_users.manage_addUser(<parameters>)

   - a transaction will not be committed to the Zope object database
     until you call the global function "get_transaction().commit()",
     so changes made to the ZODB are not visible to succeeding tests
     unless that function is called explicitly.

   - As a part of your tearDown, make sure to call
     "app._p_jar.close()".  This closes the database connection
     cleanly.

  For more information on operating on Zope programatically by
  "importing" it, see Michel Pelletier's "The Debugger Is Your
  Friend" at
  http://www.zope.org/Members/michel/HowTos/TheDebuggerIsYourFriend

  Sometimes, just importing Zope isn't enough.  For example, it's
  often not possible to obtain the results of a DTML or Python method
  by simply calling it from your running code without doing lots of
  trickery by establishing fake REQUEST object to pass in to it, as
  Zope expects a REQUEST object to exist in the context of a web
  request (which a DTML method is somewhat logically designed to
  serve).

  If you find yourself getting bogged down while writing unit tests by
  Zope's refusal to run certain methods due to missing state (like
  REQUEST), it's useful to know about the "debug" method of the Zope
  package.  This method allows you to simulate a web request, which
  generally provides all the state necessary to run methods which
  depend on web requests, and returns the results of the web request
  as it would be seen in by a web browser.  To use the Zope debug
  method, do the following:

   - add the lib/python path to your PYTHONPATH (via sys.path.insert())

   - 'import ZODB'

   - 'import Zope'
  
   - 'Zope.debug('/a/url/representing/a/method?with=a?couple=arguments',
                 u='username:password', s='silent', e={'some':'environment',
                 'variable':'settings'})

  The "silent" option causes Zope not to print anything.  You can set
  your python's stdout to a file or a file-like object to capture the
  output if you do not set the silent flag.

  In Zope versions before 2.2.2, all calls to Zope.debug commit the
  transaction represented by the call to Zope.debug by default.  This
  can pose problems for unit testing, as the state of the environment
  should not be effected by prior tests.  A solution should be
  available by the release of Zope 2.3.

 Administrivia

  Unit test scripts found in the Zope source code currently make use
  of the PyUnit unit testing framework, available from
  http://pyunit.sourceforge.net, written by Stephen Purcell (thanks
  Stephen!).  PyUnit is based on the JUnit testing framework for Java
  (written by Kent Beck and Erich Gamma), which in turn was based on a
  testing framework designed for Smalltalk (also written by Kent
  Beck).

  Unit testing is a primary tenet of "Extreme Programming", a software
  development methodology designed to faciliate the rapid production
  of high quality code with a minimum of developmental ceremony.  For
  more information on unit tests as they relate to Extreme
  Programming, see http://c2.com/cgi/wiki?UnitTestsDefined.  Although
  Digital Creations has not embraced the entire spectrum of Extreme
  Programming methodologies in its software development process, we've
  found unit tests a way to speed development and produce
  higher-quality code.




