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Both positive and negative testing play an important role. Positive testing ensures that the application does what it is implied for and performs each function as expected. Negative testing is opposite of positive testing. Negative testing discovers diverse approaches to make the application crash and handle the crash effortlessly. Example
The false positive rate (FPR) is the proportion of all negatives that still yield positive test outcomes, i.e., the conditional probability of a positive test result given an event that was not present. The false positive rate is equal to the significance level. The specificity of the test is equal to 1 minus the false positive rate.
Test data are sets of inputs or information used to verify the correctness, performance, and reliability of software systems. Test data encompass various types, such as positive and negative scenarios, edge cases, and realistic user scenarios, and aims to exercise different aspects of the software to uncover bugs and validate its behavior.
[1] [2] For example, the happy path for a function validating credit card numbers would be where none of the validation rules raise an error, thus letting execution continue successfully to the end, generating a positive response. Process steps for a happy path are also used in the context of a use case. In contrast to the happy path, process ...
A negative test can relate to: Negative diagnostic test, a medical test in which the target parameter that was evaluated was not present; Negative test variation, a software stress test designed to determine the response of the system outside of normal parameters; Negative testing
The positive predictive value (PPV), or precision, is defined as = + = where a "true positive" is the event that the test makes a positive prediction, and the subject has a positive result under the gold standard, and a "false positive" is the event that the test makes a positive prediction, and the subject has a negative result under the gold standard.
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