4.0 KiB
4.0 KiB
Specification Quality Checklist: LLM Table Translation Service
Purpose: Validate specification completeness and quality before proceeding to planning
Created: 2026-05-08
Updated: 2026-05-08 (post-review)
Feature: spec.md
Content Quality
- No implementation details (languages, frameworks, APIs) leak into spec.md
- Focused on user value and business needs — translation workflow, dictionary management, scheduling for operational stakeholders
- Written for non-technical stakeholders — uses business terminology throughout
- All mandatory sections completed (User Scenarios, Clarifications, Requirements, Key Entities, Success Criteria, Assumptions, Access Control Matrix)
UX Consistency
- Functional requirements fully support all flows in ux_reference.md (Flows A–G, updated for Superset API execution)
- Error handling requirements match all 'Error Experience' scenarios (A–I) in ux_reference.md
- No requirements contradict the defined User Persona or Context
- UX principles (preview gates manual runs, traceability, graceful degradation, cost awareness) are reflected in functional requirements
- INSERT execution via Superset API is consistent across spec, UX, quickstart, and contracts
Requirement Completeness
- No [NEEDS CLARIFICATION] markers remain — all post-review ambiguities resolved in Clarifications sessions
- Requirements are testable and unambiguous — each FR describes a specific, verifiable behavior
- Success criteria are measurable — 15 SC items include specific percentages, time bounds, or coverage targets
- Success criteria are technology-agnostic (no implementation details)
- All acceptance scenarios are defined — 7 user stories with 41 total acceptance scenarios
- Edge cases are identified — 19 edge cases covering NULL values, missing tables, large datasets, concurrency, LLM failures, composite keys, dictionary duplicates, schedule overlaps, retention gap, Superset API errors, SQL injection
- Scope is clearly bounded — covers configuration, preview quality gate, execution via Superset API, history, dictionary management, feedback loop, and scheduling; dialect-aware SQL generation (PostgreSQL/Greenplum, ClickHouse)
- Dependencies and assumptions identified — 22 assumptions
Feature Readiness
- All functional requirements have clear acceptance criteria (mapped to user story acceptance scenarios)
- User scenarios cover primary flows: Configuration (P1) → Preview Quality Gate + Dictionary (P2) → Superset API Execution + Feedback + Scheduling (P3) → Audit (P4)
- Feature meets measurable outcomes defined in Success Criteria
- No implementation details leak into specification
- Feature aligns with existing ss-tools architecture patterns (plugin system, Superset integration, LLM provider, scheduler, notification, RBAC)
- Contradictions resolved: FR-023 (snapshot isolation), dictionary conflict model (overwrite/keep existing), preview model (quality gate), auto-insert for scheduled runs (after first successful manual run)
- New entities added: TranslationBatch, TranslationPreviewSession, TranslationPreviewRecord, MetricSnapshot
- Event model expanded: nullable run_id, terminal states (succeeded/partial/failed/cancelled/skipped), pre-run events
- Access control matrix defined with ownership constraints
- Dialect-aware SQL generation declared (PostgreSQL/Greenplum + ClickHouse, detected from Superset connection)
- Retention gap resolved: MetricSnapshot persistence before event pruning
Notes
- Post-review session resolved 3 critical contradictions and 10+ medium gaps.
- FR count: 49 | SC count: 15 | Key entities: 10 | Edge cases: 19
- Preview model clarified as quality gate (not row-level approval for all rows).
- INSERT execution standardized on Superset
/api/v1/sqllab/execute/API. - All manual copy/paste SQL Lab references removed from spec, UX, quickstart, and contracts.
- Specification is ready for the next phase (
/speckit.planre-validation or/speckit.implement).