Suppose that the functional testing is automated upon management decision. This means that it becomes possible to run more tests for the same period of time during which you can execute only several manual tests. However, these tests must be selected very carefully: this is due to the fact that probably there is still a multitude of possible input values therefore it is hardly possible to conduct complete testing. As a rule, most functions have lots of possible parameter values (arguments), so even a computer is unable deal with them all.
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Of course, be sure to check the boundary values, but now you can afford the luxury of conducting a much more thorough testing. How to determine which tests are the best ones? Most often, to succeed in the selection procedure, sensitivity analysis is used. This process is as follows:
- First of all, you get a general idea of how the function behaves by calculating its value to a number of parameters, which are located along the entire domain set.
- Then, you will be looking for the domain set in which small changes in the arguments cause significant jumps in the resulting values. (For example, at the approach of X to 90 degrees, the value of tan (X) increases dramatically.) These are the most error prone areas.
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- The values obtained from testing a programmable function and its prototype, may not be necessarily the same or be only partially similar to each other. If during the calculations, floating-point operations are performed, then you will not manage to avoid rounding or truncating the results, and hence finding small differences. Usually it is not a big problem. You only need to properly determine the permissible rounding errors so that to be able to identify differences exceeding them ( or the threshold) due to different reasons.
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