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Heuristic

As explained in Software Testing with Generative AI

A practical rule or strategy for examining a system from a particular perspective without requiring a complete formal analysis.

A heuristic is a practical rule or strategy for examining a system from a particular perspective without requiring a complete formal analysis. Exploratory testers often rely on unconscious heuristics developed through experience, then use explicit heuristics to generate further ideas when their thinking begins to stall. section 8.2.3 section 5.3.2

Mnemonics turn perspectives into prompts for investigation:

  • PAOLO examines Portrait orientation, Audio artifacts, Objects in view, Landscape orientation, and Overlays. section 8.2.3
  • SFDIPOT examines Structure, Function, Data, Interfaces, Platform, Operations, and Time. section 5.3.2
  • A heuristic can be applied to a whole product or to a smaller component or user flow, prompting targeted questions about risks and behavior. section 5.3.2

From heuristic to test idea

While testing a booking calendar, keyboard navigation exposed focus failures in overflowing pop-ups. Applying PAOLO could prompt checks for portrait and landscape rendering, overlays, visible objects, and audio artifacts. Applying SFDIPOT could prompt a separate investigation of how time affects the booking flow. section 8.2.3 section 5.3.2

How Software Testing with Generative AI covers it

How deep it goes
Moves from experienced intuition to explicit mnemonic-driven prompts, but retains dependence on personal experience and bias.
Examples
Uses PAOLO, SFDIPOT, booking-calendar navigation, overflowing pop-ups, and time-based risk prompts.
What it assumes
Assumes exploratory-testing experience for unconscious heuristics but provides explicit mnemonics for generating ideas from zero in a chosen perspective.
What you can do after
Apply it via PAOLO or SFDIPOT to generate focused test ideas, risks, and alternative questions about a product or flow.
How it connects
Connects exploratory testing, mental models, formal modeling techniques, risk analysis, and prompting.

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