Regression Verification
A testing process that confirms new changes have not broken existing functionality; in AI-assisted programming it is used to detect side effects unintentionally introduced by the model, and is often combined with the small-step edit loop for continuous verification.
Timeline (last 90 days)
Regression verification confirms after a change that existing functionality has not been broken, usually covering a fixed set of key scenarios.
What smoke testing and regression verification have in common is that their judgment criteria are relatively clear (a certain element appears in the interface, the flow reaches a certain step), making them suitable for AI agents to execute; whereas experience issues, boundary conditions and exception paths that require subjective judgment are still better suited to manual acceptance.
Regression verification refers to the testing process of confirming that a new change has not broken existing functionality; it is especially important in AI-assisted programming because the model may inadvertently introduce side effects, such as breaking a JavaScript selector that depends on a CSS class name when that class name is modified.
The small-step edit loop breaks regression verification down into an immediate check after each modification; it is essentially a "continuous verification" strategy whose cost is far lower than troubleshooting a mixed diff after the fact.
All Facts (4)
Regression verification confirms after a change that existing functionality has not been broken, usually covering a fixed set of key scenarios.
50%UnverifiedWhat smoke testing and regression verification have in common is that their judgment criteria are relatively clear (a certain element appears in the interface, the flow reaches a certain step), making them suitable for AI agents to execute; whereas experience issues, boundary conditions and exception paths that require subjective judgment are still better suited to manual acceptance.
50%UnverifiedRegression verification refers to the testing process of confirming that a new change has not broken existing functionality; it is especially important in AI-assisted programming because the model may inadvertently introduce side effects, such as breaking a JavaScript selector that depends on a CSS class name when that class name is modified.
50%UnverifiedThe small-step edit loop breaks regression verification down into an immediate check after each modification; it is essentially a "continuous verification" strategy whose cost is far lower than troubleshooting a mixed diff after the fact.
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