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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.
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.
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.
Acceptance testing can mean one of two things: A smoke test is used as an acceptance test prior to introducing a new build to the main testing process, i.e., before integration or regression. Acceptance testing performed by the customer, often in their lab environment on their own hardware, is known as user acceptance testing (UAT). Acceptance ...
One by one, a set is selected as test set. Then, one by one, one of the remaining sets is used as a validation set and the other k - 2 sets are used as training sets until all possible combinations have been evaluated. Similar to the k*l-fold cross validation, the training set is used for model fitting and the validation set is used for model ...
If a test has a false positive rate of one in ten thousand, but only one in a million samples (or people) is a true positive, most of the positives detected by that test will be false. The probability that an observed positive result is a false positive may be calculated using Bayes' theorem .
A rigorous test case based approach is often traditional for large software engineering projects that follow a Waterfall model. [2] However, at least one recent study did not show a dramatic difference in defect detection efficiency between exploratory testing and test case based testing. [3] Testing can be through black-, white-or grey-box ...