Claude/progress semantic check 011 cuqy3 ex6fy ru xb6 w gj nze #14
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This commit activates the semantic test-adherence feature specification by: - Moving spec from specs/future/ to specs/ (activating for implementation) - Changing Status from "Provisional" to "Draft" - Adding LiteLLM as the integration library (REQ-021) - Adding Groq provider support for free-tier CI/CD usage (REQ-022) - Adding requirement for default model configurations pinned in releases (REQ-023) - Expanding provider support: groq, anthropic, openai, ollama, vertex_ai, bedrock (REQ-036) - Setting groq as default provider (REQ-037) - Updating environment variables for all providers (REQ-042) - Fixing SPEC ID conflict: spec-coverage-linter changed from SPEC-003 to SPEC-004 The specification now contains 67 functional requirements ready for implementation. This aligns with the technical design document in technical-notes/llm-provider-selection.md. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
This commit implements the core MVP for the semantic test-adherence checker defined in SPEC-003, enabling AI/LLM-powered validation of test-requirement alignment. ## What's Added: ### Core Modules: - **semantic_test_result.py**: Result dataclasses for semantic analysis - SemanticAnalysisResult: Individual test-requirement pair analysis - SemanticTestAdherenceResult: Overall validation results with reporting - **llm_provider.py**: LLM provider abstraction layer - LLMProvider: Abstract base class for LLM providers - LiteLLMProvider: Implementation using LiteLLM library - Support for 6 providers: Groq (default), Anthropic, OpenAI, Ollama, Vertex AI, Bedrock - Default models pinned per provider (as per REQ-023) - Retry logic with exponential backoff (3 retries, as per REQ-025) - JSON response parsing with error handling - **semantic_test_analyzer.py**: Core semantic analyzer - Requirement discovery from spec files with full text extraction - Test discovery with source code and docstring extraction - Per-test-requirement pair semantic analysis using LLM - Confidence scoring and threshold-based validation - Provisional spec exclusion (consistent with check-coverage) ### CLI Integration: - Added `check-semantic-test-adherence` command to cli.py - Command-line arguments: --llm-provider, --llm-model, --threshold, --specs-dir, --tests-dir - Comprehensive help text with examples and LLM provider configuration ### Dependencies: - Added litellm package for unified LLM provider access - Updated pyproject.toml and uv.lock ### Exports: - Updated __init__.py to export new classes for library usage ## Implementation Notes: - Follows existing project patterns (reuses logic from spec_coverage_linter) - Defaults to Groq provider for free-tier CI/CD usage (REQ-037) - All code passes ruff linting and formatting checks - Existing test suite passes (131 tests) ## What's Not Yet Implemented (Future Work): - Comprehensive test suite for semantic analyzer (TEST-001 to TEST-010) - Configuration file support in config.py (REQ-031) - Batching optimization for multiple tests (REQ-030) - Caching support (REQ-044-045) - Alternative output formats: JSON, Markdown (REQ-052-054) - Concurrent request pooling (REQ-043) ## Requirements Addressed: Core functionality implements: - REQ-001 to REQ-020: Requirement/test discovery and semantic analysis - REQ-021 to REQ-029: LLM integration with LiteLLM - REQ-031 to REQ-037: Basic configuration (CLI args) - REQ-046 to REQ-048: Reporting - REQ-055 to REQ-057: Exit codes - REQ-058 to REQ-062: Error handling - REQ-063 to REQ-067: Integration with existing tools 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
This commit enhances the CI pipeline with two new validation steps: 1. **Unique Spec IDs Validation**: - Validates that all SPEC IDs and requirement IDs are unique - Prevents duplicate identifiers across the codebase - Required check (must pass for CI to succeed) 2. **Semantic Test-Adherence Validation (Optional)**: - Validates that tests semantically test their linked requirements using AI/LLM - Uses Groq provider with GROQ_API_KEY from GitHub secrets - Set as optional with `continue-on-error: true` since API key may not be configured - Provides early feedback when API key is available Both checks are added to the lint job for comprehensive validation. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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