QA Outsourcing As A Service For Functional Equivalence Testing

QA Outsourcing As A Service For Functional Equivalence Testing

Functional equivalence testing as it is and its automation

Functional equivalence testing focuses of comparing the different programs in calculating the same mathematical function. This term has nothing to do with the equivalence classes. If both of the programs produce identical results while calculating the same function, it means that equivalent calculation methods are used within them. QA outsourcing experts tend to automate functional equivalence testing to achieve better productivity, reduce manual testing efforts, costs due to faster quality assurance cycles, speed up time to market..   

Suppose that you test a program that calculates the mathematical function and prints the outcome. This can be a simple trigonometric function or much more complex mathematical function inverting a matrix or returning coefficients for the construction of the curve that reflects a certain set of data. Usually in such cases it is possible to find another program that performs the same steps, and at the same time which is sufficiently reliable and time-tested. Both programs are offered to process the same set of input data, and if the results match, it means that the software under test is working properly.

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Automating functional equivalence testing

Wherever possible use the functional equivalence testing method, and it will be the best choice. And that is why.

• First of all, you do not have to calculate the values ​​manually. If the function is complex, the automation can save you a tremendous amount of time and help to avoid errors, so often encountered in non-automated calculations.
• The comparison process will probably be automated. The simplest way to actualize this  may be withdrawal and placing of the calculation results in files and their subsequent comparison with the corresponding software. The computer will compare the files faster and more accurately than you. Also, you can define allowable differences in the results – for example, the rounding error.

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