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Nose supports parametric/generative tests, http://readthedocs.org/docs/nose/en/latest/writing_tests.html#test-generators For example: def checker(combination): # do something to verify the combination assert 'A' in combination def test_generator(): options = ['A', 'B', 'C'] for o in options: yield checker, o for p in ...


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I was able to accomplish this by writing a custom TestClassExtensionAttribute and then calling a custom ITestMethodInvoker from the TestExtensionExecution. I answered a similar question on SO about this: http://stackoverflow.com/a/14673329/573218 I have posted the code on GitHub and am working on cleaning it up a bit: ...


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There are lots of ways to do this; some of the data points I have used in the past are: Build breakage: is the automated build compilation/test failure rate going up or down? Assuming an Agile development model, are the user stories complete and have they been reviewed by QA + stakeholders Has the code freeze been effective? Are developers still checking ...


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Since this question was cross-posted to stackoverflow, I'll cross-post my answer: Unfortunately, the use of test templates more-or-less require that the data is hard-coded in the test case. However, the test template is not much more than a wrapper around a for loop. You could do something like this: | | ${database_rows}= | Run sql query | | ... | Select * ...


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If I understand you situation correctly you have a data-driven test, and you want to be able to run that data-driven test using the arguments in the 'datafile' against one or more services with varying locations. One approach may be to read in a file that lists the desired services (location) at runtime into an array, then loop through each element ...


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I don't think that there can be such a concept as an "acceptable" defect arrival rate, in the sense of a target number as you typically want to discover them as fast as possible. So, in english, you want to find as many bugs as you can. The key way to read that metric is that you want a consistent arrival rate over time, i.e. you want the defect arrival ...



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