What problem does it solve? When input conditions have complex logical relationships or inputs span multiple independent dimensions, hand-written test cases easily miss combinations or include impossible ones. This Skill provides two black-box techniques—Cause-Effect Graphing and the Classification Tree Method—that systematically reduce combination spaces into a complete, non-redundant test set. ## Core Features & Use Cases - Cause-Effect Graph: Models inputs (causes) and outputs (effects) with AND/OR/NOT logic gates, then mechanically derives a decision table so no feasible rule is missed and infeasible combinations are excluded. - Classification Tree Method: Structures multi-dimensional inputs (e.g., format × size × color mode) into classification axes subdivided into equivalence classes, then selects leaf combinations using full, pairwise, or priority strategies. - Coverage criteria and gap checks: Defines explicit completeness rules—every feasible decision-table rule covered, each cause shown to affect the result, every leaf appearing at least once—so coverage holes can be detected by reverse-checking the test set. - Use Case: Testing an ATM withdrawal rule where card validity, balance, and daily limit combine logically; the cause-effect graph derives exactly the four decision-table rows needed, expressed as a Vitest it.each table. ## Quick Start Ask the AI to apply the cause-effect graph or classification tree method from this catalog to derive test cases for a function with complex input logic.
