Negative testing
Negative testing is a technique that evaluates whether software correctly handles invalid, unexpected, unavailable, or prohibited inputs and conditions.
Effective Conversational AI
Negative testing evaluates whether a system correctly rejects, declines, or otherwise handles inputs that it should not answer or process.
- How it is used
- Evaluates whether a conversational solution recognizes when it should not attempt to answer or process an utterance.
- How deep it goes
- Distinguishes off-topic and ambiguous utterances from ordinary classifier tests, but does not assign them golden intents.
- Examples
- Uses utility-account questions, pop-culture trivia, and ambiguous utterances such as “account,” “that one,” and “2.”
- What you can do after
- Diagnose whether a bot appropriately rejects, defers, or otherwise handles off-topic and unclear user utterances.
Read how Effective Conversational AI explains negative testing →
Data-Oriented Programming: Reduce software complexity
A negative test case verifies that software behaves correctly when given invalid input or an input that should cause failure.
- How it is used
- Strengthens unit tests for mutations by checking behavior when an operation should fail.
- How deep it goes
- Demonstrates setup, execution, exception capture, and comparison, while deferring broader invalid-input categories.
- Examples
- Tests adding an already-existing member and expects the exact "Member already exists." exception.
- What you can do after
- Diagnose failure handling by verifying that invalid mutation input produces the expected exception.
Read how Data-Oriented Programming: Reduce software complexity explains negative testing →
Related concepts
- testuses · A test is a procedure or check that evaluates behavior using specified inputs or evidence against expected results or criteria.
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