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https://github.com/azaion/ai-training.git
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Oleksandr Bezdieniezhnykh
40 lines
2.4 KiB
Markdown
40 lines
2.4 KiB
Markdown
# Phase 1: Input Data & Expected Results Completeness Analysis
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**Role**: Professional Quality Assurance Engineer
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**Goal**: Assess whether the available input data is sufficient to build comprehensive test scenarios, and whether every input is paired with a quantifiable expected result.
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**Constraints**: Analysis only — no test specs yet.
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## Steps
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1. Read `_docs/01_solution/solution.md`
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2. Read `acceptance_criteria.md`, `restrictions.md`
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3. Read testing strategy from `solution.md` (if present)
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4. If `DOCUMENT_DIR/architecture.md` and `DOCUMENT_DIR/system-flows.md` exist, read them for additional context on system interfaces and flows
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5. Read `input_data/expected_results/results_report.md` and any referenced files in `input_data/expected_results/`
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6. Analyze `input_data/` contents against:
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- Coverage of acceptance criteria scenarios
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- Coverage of restriction edge cases
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- Coverage of testing strategy requirements
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7. Analyze `input_data/expected_results/results_report.md` completeness:
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- Every input data item has a corresponding expected result row in the mapping
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- Expected results are quantifiable (contain numeric thresholds, exact values, patterns, or file references — not vague descriptions like "works correctly" or "returns result")
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- Expected results specify a comparison method (exact match, tolerance range, pattern match, threshold) per the template
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- Reference files in `input_data/expected_results/` that are cited in the mapping actually exist and are valid
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8. Present input-to-expected-result pairing assessment:
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| Input Data | Expected Result Provided? | Quantifiable? | Issue (if any) |
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|------------|--------------------------|---------------|----------------|
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| [file/data] | Yes/No | Yes/No | [missing, vague, no tolerance, etc.] |
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9. Threshold: at least 75% coverage of scenarios AND every covered scenario has a quantifiable expected result (see `.cursor/rules/cursor-meta.mdc` Quality Thresholds table)
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10. If coverage is low, search the internet for supplementary data, assess quality with user, and if user agrees, add to `input_data/` and update `input_data/expected_results/results_report.md`
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11. If expected results are missing or not quantifiable, ask user to provide them before proceeding
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## Blocking
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**BLOCKING**: Do NOT proceed to Phase 2 until the user confirms both input data coverage AND expected results completeness are sufficient.
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## No save action
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Phase 1 does not write an artifact. Findings feed Phase 2.
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