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Negative testing is a method of testing an application or system to improve the likelihood that an application works as intended/specified and can handle unexpected input and user behavior. [1] Invalid data is inserted to compare the output against the given input.
Regression testing is performed when changes are made to the existing functionality of the software or if there is a bug fix in the software. Regression testing can be achieved through multiple approaches; if a test all approach is followed, it provides certainty that the changes made to the software have not affected the existing functionalities, which are unaltered.
Complementarily, the false negative rate (FNR) is the proportion of positives which yield negative test outcomes with the test, i.e., the conditional probability of a negative test result given that the condition being looked for is present. In statistical hypothesis testing, this fraction is given the letter β.
The negative predictive value is defined as: = + = where a "true negative" is the event that the test makes a negative prediction, and the subject has a negative result under the gold standard, and a "false negative" is the event that the test makes a negative prediction, and the subject has a positive result under the gold standard.
VCR testing, also known as "playback testing" or "record/replay" testing, is a testing technique for increasing the reliability and speed of regression tests that involve a component that is slow or unreliable to communicate with, often a third-party API outside of the tester's control.
Regression testing focuses on finding defects after a major code change has occurred. Specifically, it seeks to uncover software regressions, as degraded or lost features, including old bugs that have come back. Such regressions occur whenever software functionality that was previously working correctly, stops working as intended.
Fantasy football analyst Pranav Rajaram identifies six players who could be set to see regression (positive or negative) in their production in comparison to last season.
False negatives produce serious and counter-intuitive problems, especially when the condition being searched for is common. If a test with a false negative rate of only 10% is used to test a population with a true occurrence rate of 70%, many of the negatives detected by the test will be false.