[TIP] Meta-test methods...
dgou at mac.com
Sat Apr 25 07:52:39 PDT 2009
On or about Fri, 2009-04-24 at 20:11 -0400, Victoria G. Laidler indited:
> So when I test, I want to make sure my tests probe most
> regions of the potential parameter space of input data. I use
> generated tests to run exactly the same test with varying
My problem space is different, but the testing approach seems similar.
On or about 2009 Apr 24, at 8:30 PM, Robert Collins indited:
> I use a form of dependency injection to do this: I write one test that
> takes as parameters the stuff it needs to test. With unittest, I
> wrote a
> little library 'testscenarios' to make it nice and easy to use. Hmm,
> just remembered, I need to upload it to pypi.
> Anyhow - https://launchpad.net/testscenarios is the homepage, README
... <example elided -dwp>
> and so on. I think this nicer than yielding inner functions - but its
> arguably just a different spelling.
I'm not sure I've grok'd it all, but from what I understand those
scenarios are all "predefined" before the tests themselves run?
Looking at lines 210-240 of the README, it seems that the scenario
generation, can, like all python modules, compute the scenarios as the
module is loaded? So it is dynamic in the sense of querying the
environment in which the test code is being loaded?
(Our Python testing environments are simpler in the sense that we
always have the needed Python imports available.)
Our devices under test have an extensive set of potential capabilities
of variable capacity which we've abstracted away into "device
personalities"... i.e. table-driven test code, with lots of tables. :)
Many of our tests are simple and don't need Scenarios.
Our most involved tests have our own "create a list of scenarios" phase:
Sometimes those scenarios are pre-determined, and thus your pre-
computable Scenarios would be a good fit.
Sometimes those scenarios are dependent on the specific device-
under-test's capability's capacity. Devices with a certain capability
can span from the small/cheap to the large/expensive. The actual range
to be tested is not known until the device itself can be interrogated.
The most "interesting" tests involve interactions between the
different capabilities along the varying ranges of those capabilities.
Ok, enough background... :)
The problem I need to solve is that a test method only reports one
status. In the context of one test method with hundreds of scenarios,
unless every scenario passes, we lose information about the nature,
severity, and extent of the problems/failures. Maybe the first few
devices we tested had dozens of scenario failures in a particular
test. A new version of the device might fix some issues and have fewer
(scenario) failures. However, that test's results won't look any
different. Worse case is if only some of original issues are fixed and
new issues are introduced, and so even the total number of scenario
failures might remain the same. For these kinds of tests we have to
turn on tracing and manually check the results. :(
I doubt that Titus or Vicki has this exact problem :). I think Vicki
has a similar problem, if I understood her... I'm not sure I
understand Titus' use cases, nor do I know what drove py.test and nose
to put in their capacity for dynamic test method creation.
I remain hopeful that we have a common enough problem that we can
share a common solution. :)
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