Testing has been traditionally viewed as an exercise of observing and commenting on the behavior of an “application under test“. Testers have been always groomed to be honest to the “requirements” that form the basis of solutions. Then came the era of scale and the consequent resource contention issues that software (and associated hardware). “Non functional Requirements” also got added as a new dimension to testing. But the focus still remained the same – application behavior. As applications grew to become integrated solutions and then platforms, the philosophy of testing remained the same.
A parallel view for testing evolved in the meantime – primarily led by the data ware house world – a concept that looked at software as a “means” to achieving data transformation, processing and reporting. Testing can also be viewed as data in motion. Of course, the functions that retrieve, store, transmit, process and syndicate/present data are as important as the data itself from a testing standpoint, but then – as long as the data moves in an expected fashion from the source to the end under a spectrum of conditions, the product can be deemed “Quality Assured“.
A few aspects of data centered testing – One needs a thorough understanding of
There are several more of such dimensions that could be used to build a framework for testing.
Is data centered testing a better than traditional app focused testing? Some key points in its favor
The next level of detail could be in how one could achieve a reasonable level of effectiveness of implementation of such a concept on the ground. That would involve the right methods to sample the data in motion, comparison mechanisms, test data management and test results correlation to business requirements.
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