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The first strategy that we may come up with is data-driven testing. It is a technique where a well-defined test script is executed multiple times, taking into account a "table" of parameters and corresponding values.

Usually, data-driven testing is used as a way to inject data to test automation scripts but it can also be used to manually perform the same test multiple times against different data iterations.

However, the exact combination of parameter values to be used is beyond the scope of data-driven testing. Usually, testers include parameter value combinations that represent examples coming as a direct consequence of acceptance criteria, from well-known "happy paths", or from the production data.

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Before we talk about the testing option we consider optimal, let’s look at the underlying practical evidence.

Studies such as “PRACTICAL COMBINATORIAL TESTING, 2010”, presented by NIST, indicate “Estimating t-Way Fault Profile Evolution During Testing” and “Practical Combinatorial Testing” (presented by the National Institute of Standards and Technology in 2017 and 2010 respectively) indicate that the vast majority of defects (67%-93%) related to input values are due to a problem in either one parameter value (single-value fault) or a combination of two parameter values (2-way interaction fault).

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