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Automatic debugging is usually invoked when a test fails in one situation, but succeeds in another.
If a test fails because of problems with design or execution, it merits a brief write-up.
For example, a test fails in one version of the program (or scheduler), but succeeds in another.
When a test fails unexpectedly, developers can just revert to the last stable version and start over.
That means if a test fails, not only are you getting the results of that fail, you can actually launch the exact build, click around and reproduce the bug yourself.
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If a test failed, the postulate could be revised, and if the test then succeeded, the findings would be added to the body of psychological science.
If data protection regulators cannot stop the collection of vast amounts of data -- a test failed long ago -- then competition regulators must step up to protect consumers.
If a test failed giving no result on the sample (analytical failure), then the lab received 1.00 point for that sample.
Table 11 also shows that a t test fails to reject the null hypothesis of continuity.
If a single test fails, then the theory is falsified.
Moreover, if an acceptance test fails, the delivery data remains unchanged and the SLA will probably not be met, resulting in penalties.
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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