decision-table-testing

Decision Table Test Design

Organize conditions, rules, actions, and outcomes into an auditable set of rule combinations, and output DTT-## findings. It only organizes traceable test design candidates; it does not execute tests, nor does it write the design checklist as coverage, pass, or release evidence.

When to Use

• When Decision Table Test Design candidates need to be extracted from business rules, conditions, actions, exceptions, priorities, scope of applicability, and existing test materials.
• When the rationale for selection, applicable constraints, evidence gaps, and minimum verification actions need to be explained.
• When materials are incomplete but a limited initial version still needs to be delivered, with blocked or unassessed boundaries clearly stated.

Not applicable to directly executing tests, generating rules without sources, replacing a complete test strategy, or accepting risk on behalf of a Human.

Output Format Options

• Default output is Markdown; tables, JSON, or CSV are used only when the user explicitly needs them or the existing delivery format requires them.
• Keep static analysis, unexecuted items, evidence status, and Human decisions separate; when there is no execution evidence, keep them as unassessed, blocked, or NOT_RUN.

How to Use

1. Read prompts/decision-table-testing.md, and provide the goal, scope, materials, environment, and existing evidence.
2. First complete the six-category input audit: known, missing, conflicting, stale, out_of_scope, and assumptions.
3. Use DTT-## to record rule ID, condition combination, scope of applicability, action/outcome, exceptions, source evidence, and verification method, retaining source, evidence status, impact/priority, responsible role, closure conditions, and verification method.
4. When materials conflict, constraints are missing, or there is no execution evidence, retain both sides and the open questions.

Domain Execution Points

• Discretize continuous conditions into mutually exclusive intervals/categories according to the source rules, and list condition domains, actions, exceptions, and priorities; do not directly fill ambiguous conditions with true/false.
• Deliver the full combination → matching rules → action table and the overlap/missing list; compression must not obscure undefined behavior.
• When forming domain candidates, you must read and follow the domain operations and determination process of prompts/decision-table-testing.md , retaining legitimate controls, evidence gaps, and verification actions item by item.

Core Constraints

• Do not execute the tests in the table, do not assume rules that were not provided, and do not treat the number of table rows as proof of coverage.
• File existence, name matching, design declarations, or Eval configuration do not equal real execution evidence.
• Mark unknown items as unassessed, blocked, or pending confirmation; do not fill them in with common sense.
• Do not modify requirements, code, test assets, or the target system.

Pre-Delivery Self-Check

• The six-category input audit has been recorded.
• Each DTT-## has a source, minimum evidence, impact/priority, responsible role, closure conditions, and verification method.
• Facts, inferences, recommendations, unexecuted items, and Human decisions have been distinguished.
• Design candidates have not been written as complete tests, execution results, coverage proof, or release conclusions.

Reference Files

• During regression, read evals/eval.yaml and the matching test cases; configuration does not prove real project results.
• For trigger verification, use evals/trigger-prompts.csv and evals/local-rules.json; when skill.selection evidence is missing, report BLOCKED.

Common Misconceptions

• When the scope or evidence is incomplete, do not write method names, file existence, or candidate counts as evidence that tests have been executed, covered, passed, or released.
• Do not use common sense to fill in rules, thresholds, data, environments, or results that were not provided; retain unassessed, blocked, and to-be-confirmed items.
• Do not expand this Skill's specialized candidates into a complete strategy, complete test cases, actual execution, or release decisions.

Best Practices

• Complete the six categories of input audit first, then choose the smallest, traceable, and verifiable discovery scope.
• For each finding, retain the source, evidence status, impact/priority, responsible role, closure conditions, and verification method, and clearly state the remaining risk.
• Write verification recommendations as next-step actions, and do not upgrade the Skill package structure, candidate counts, or local Eval configuration into real quality conclusions.