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Taken together, the results above are strong evidence that our new algorithm and its implementation are correct, computationally efficient and capable of analysing large datasets.
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Here the assumption is that the underlying implementation is correct.
Here we must ask what it means to say that the implementation is correct.
Verification means making sure the implementation is correct, ensuring that it conforms to the requirements recorded in the specification.
Furthermore, there is a hidden assumption that the underlying implementation is correct: at best, these empirical methods tell us something about the whole system.
We figured rather than have suppliers trust us, we found it better to give them the ability to review on their own, have the experts review if its implementation is correct.
To make sure that our implementations are correct and the testing procedure is valid, we compared our results on the DIP_MIPS_iPfam dataset with those reported in Bleakley et al. (2007) as the size of this dataset is most similar to the one used by them.
Furthermore, the use of formal verification provides the means to ensure that implementations are correct-by-construction.
You can verify on your own that the implementation, that the algorithms, are correct, that there are no back doors, which is of course always a concern in the industry.
Model verification ensures that the computer programming and implementation of the conceptual model are correct [38].
In order to verify that a given implementation conforms to such loosely defined specifications, the typical approach is to verify the implementation to be correct within well defined limits of time tolerance.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com