test(cli): add comprehensive test coverage for benchmark and harbor commands

[chambridge]

Description

Add 50 new unit tests covering CLI command functionality:

• tests/unit/test_cli_benchmark.py: 27 tests for benchmark command (80% coverage)
• tests/unit/test_cli_harbor.py: 23 tests for harbor command (96% coverage)

Key improvements:

• Project coverage: 64.5% → 67.0% (+2.5pp)
• cli/harbor.py: 20% → 96% (+76pp)
• cli/benchmark.py: 20% → 80% (+60pp)
• All 50 tests pass with comprehensive module documentation
• Uses actual data models for type safety
• Covers success paths, error handling, and edge cases

Also updates .gitignore to exclude coverage.json and .claude/settings.local.json

Type of Change

• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)
• Documentation update
• Refactoring (no functional changes)
• Performance improvement
• Test coverage improvement

Related Issues

Fixes #
Relates to #

Changes Made

• tests/unit/test_cli_benchmark.py: 27 tests for benchmark command (80% coverage)
• tests/unit/test_cli_harbor.py: 23 tests for harbor command (96% coverage)

Testing

• Unit tests pass (pytest)
• Integration tests pass
• Manual testing performed
• No new warnings or errors

Checklist

• My code follows the project's code style
• I have performed a self-review of my own code
• I have commented my code, particularly in hard-to-understand areas
• I have made corresponding changes to the documentation
• My changes generate no new warnings
• I have added tests that prove my fix is effective or that my feature works
• New and existing unit tests pass locally with my changes
• Any dependent changes have been merged and published

Screenshots (if applicable)

Additional Notes

[github-actions]

AgentReady Code Review: PR #266

Overview

This PR adds comprehensive test coverage for CLI benchmark and harbor commands with 50 new unit tests. The implementation demonstrates strong testing practices with 80% coverage for cli/benchmark.py and 96% coverage for cli/harbor.py.

AgentReady Attribute Assessment

✅ Strengths (Passing Attributes)

1. Test Coverage (Tier 1) - EXCELLENT

• Impact: +2.5pp project coverage (64.5% → 67.0%)
• Module Coverage:
  • cli/harbor.py: 20% → 96% (+76pp)
  • cli/benchmark.py: 20% → 80% (+60pp)
• Test Quality: Uses actual data models (not mock dicts), covers success/failure paths, edge cases

2. Code Documentation (Tier 2) - PASS

• Comprehensive module docstrings explain test strategy, coverage targets, fixtures
• Each test class and method has clear, descriptive docstrings
• Fixtures document their purpose and data structure

3. Code Structure (Tier 2) - PASS

• Well-organized into logical test classes (TestBenchmarkCommand, TestRunTbench, etc.)
• Proper separation of CLI command tests vs internal helper function tests
• Clean fixture organization with reusable components

4. Type Safety (Tier 3) - PASS

• Uses actual data models (HarborComparison, HarborRunMetrics, HarborTaskResult)
• Avoids brittle mock dictionaries in favor of proper type instantiation
• Example: Lines 81-122 in test_cli_benchmark.py use HarborRunMetrics constructor

5. Error Handling (Tier 2) - PASS

• Tests exception scenarios: missing API keys, invalid inputs, parse failures
• Validates proper error messages and exit codes
• Examples: test_run_tbench_missing_api_key, test_compare_parse_results_failure

⚠️ Areas for Improvement

1. Security: API Key Handling (Tier 1) - MINOR ISSUE

Location: test_cli_benchmark.py:307-333, test_cli_benchmark.py:517-538

Issue: Tests modify os.environ without proper isolation, potential test pollution

# Current approach (risky)
old_key = os.environ.get("ANTHROPIC_API_KEY")
if old_key:
    del os.environ["ANTHROPIC_API_KEY"]
try:
    # test code
finally:
    if old_key:
        os.environ["ANTHROPIC_API_KEY"] = old_key

Recommendation: Use @patch.dict("os.environ", {}, clear=False) or pytest's monkeypatch fixture:

# Better approach
@patch.dict("os.environ", {"ANTHROPIC_API_KEY": ""}, clear=False)
def test_run_tbench_missing_api_key(self, tmp_path):
    # Test code - no manual cleanup needed

Impact: Low severity but important for test reliability in parallel execution

2. Test Determinism (Tier 3) - GOOD PRACTICE

Strength: Fixed timestamps for test comparisons (e.g., created_at="2024-01-01T12:00:00")

Recommendation: Consider freezing time for all tests that depend on timestamps:

@patch("agentready.cli.harbor.datetime")
def test_compare_basic_execution(self, mock_datetime, ...):
    mock_datetime.now.return_value = datetime(2024, 1, 1, 12, 0, 0)
    mock_datetime.now.return_value.strftime.return_value = "20240101_120000"

3. Hardcoded Magic Numbers (Tier 4) - MINOR

Location: Multiple test methods checking args[3], args[4], etc.

Example: test_cli_benchmark.py:166

args, kwargs = mock_run.call_args
assert args[3] is True  # verbose parameter

Recommendation: Use named tuple unpacking or constants:

# Better
repo_path, subset, model, verbose, timeout, output_dir, skip_preflight = mock_run.call_args[0]
assert verbose is True

Impact: Improves maintainability if function signature changes

4. gitignore Updates (Tier 2) - PASS

Changes: Added coverage.json and .claude/settings.local.json

Validation: ✅ Both are appropriate exclusions:

• coverage.json: Generated artifact
• .claude/settings.local.json: User-specific configuration

🔍 Code Quality Assessment

Test Architecture

• Pattern: Click CliRunner with isolated filesystem ✅
• Mocking Strategy: External dependencies mocked, internal logic tested ✅
• Fixtures: Reusable, well-documented ✅

Edge Case Coverage

• ✅ Missing files/directories
• ✅ Invalid inputs
• ✅ Missing environment variables
• ✅ Exception handling
• ✅ Symlink creation failures (Windows compatibility)

Best Practices

• ✅ Uses @patch for external dependencies
• ✅ Tests both success and failure paths
• ✅ Validates output messages and exit codes
• ✅ Proper cleanup in fixtures (context managers, try/finally)

📊 AgentReady Score Impact

Estimated Score Change: 80.0 → 81.5 (+1.5 points)

Contributing Factors:

• Test Coverage improvement: +2.5pp project coverage
• Code Quality: Excellent test documentation and structure
• Maintainability: Clear test organization, reusable fixtures

Certification: Remains Gold (75-89 range)

🎯 Recommendations

Priority: High

1. Security: Replace manual os.environ manipulation with @patch.dict for API key tests

Priority: Medium
2. Maintainability: Use named unpacking instead of positional args[N] indexing
3. Robustness: Add explicit time freezing for timestamp-dependent tests

Priority: Low
4. Documentation: Consider adding a "Testing Guide" section to CLAUDE.md referencing these tests as examples

✅ Approval Status

Recommended Action: APPROVE WITH MINOR SUGGESTIONS

Justification:

• Excellent test coverage increase (+2.5pp project-wide)
• Strong adherence to testing best practices
• Proper use of data models over mocks
• Minor suggestions do not block merge (can be addressed in follow-up)

Security: ✅ No security vulnerabilities introduced
Breaking Changes: ✅ None - purely additive tests
Documentation: ✅ Well-documented with clear docstrings

---

Review Completed: 2026-01-16
AgentReady Version: 2.23.0
Reviewer: Claude (AgentReady Review Agent)

🤖 Generated with AgentReady /review-agentready command

[github-actions]

📈 Test Coverage Report

Branch	Coverage
This PR	65.2%
Main	62.5%
Diff	✅ +2.7%

---

Coverage calculated from unit tests only

[github-actions]

AgentReady Code Review - PR #266

Overall Assessment

Status: ✅ APPROVED with minor suggestions

This PR adds excellent test coverage for CLI commands with well-structured, comprehensive unit tests. The test strategy is sound, coverage improvements are significant, and the code follows best practices.

---

📊 Coverage Impact

• Project Coverage: 64.5% → 67.0% (+2.5pp)
• cli/harbor.py: 20% → 96% (+76pp) ✨
• cli/benchmark.py: 20% → 80% (+60pp) ✨
• Total Tests Added: 50 new tests (27 benchmark + 23 harbor)

---

AgentReady Attribute Compliance

✅ Passing Attributes

1. test_coverage (Tier 1) - Excellent improvement from 64.5% to 67.0%
2. test_framework (Tier 1) - Proper use of pytest with fixtures
3. code_style (Tier 2) - Consistent naming, clear docstrings
4. type_annotations (Tier 2) - Good use of type hints in test fixtures
5. documentation (Tier 1) - Comprehensive module docstrings explaining test strategy
6. test_organization (Tier 2) - Well-organized test classes by functionality

⚠️ Suggestions for Improvement

1. Integration Tests (Tier 2)

   • Tests are all unit tests with mocked dependencies
   • Consider adding at least one integration test that calls real CLI without mocks
   • Impact: Would validate end-to-end behavior and catch integration issues

2. Edge Case Coverage (Tier 3)

   • Missing some edge cases: Windows path handling, large task lists, concurrent execution failures
   • Impact: Could miss platform-specific or load-related bugs

---

🔒 Security Analysis

✅ No Security Issues Found

1. .gitignore additions are appropriate (coverage.json, .claude/settings.local.json)
2. Test isolation: Uses CliRunner.isolated_filesystem() correctly
3. No credential exposure: Mock API keys used (test-key)
4. No command injection: All paths use Path objects

---

💻 Code Quality

Strengths

1. Excellent Documentation - Comprehensive module docstrings explaining test strategy
2. Proper Test Isolation - Uses mocks, isolated filesystems, context managers
3. Type Safety - Uses actual data models (HarborComparison, HarborRunMetrics)
4. Comprehensive Coverage - Tests success paths, error handling, edge cases

Minor Issues (All Low Priority)

1. Inconsistent Mock Setup - test_cli_benchmark.py:62-70 could use spec parameter
2. Duplicate Test Pattern - API key validation tests could be parametrized
3. Magic Numbers - Could use named constants like MOCK_TASK_DURATION_SEC

---

🧪 Test Quality Assessment

Component	Coverage	Assessment
Success Paths	✅ Excellent	All major commands tested
Error Handling	✅ Excellent	API key, invalid inputs, exceptions
Edge Cases	⚠️ Good	Missing some platform-specific cases
Integration	⚠️ Minimal	Only unit tests with mocks

---

🎯 AgentReady Attribute Scoring Impact

Estimated Overall Score Impact: +1.5 to +2.0 points (on 100-point scale)

• test_coverage: ~60/100 → ~70/100 ⬆️
• code_style: 85/100 → 90/100 ⬆️

---

✅ Approval Checklist

• No security vulnerabilities introduced
• Code follows project style guidelines
• Tests are comprehensive and well-structured
• Documentation is clear and complete
• No breaking changes
• .gitignore changes are appropriate
• All tests pass (per PR description)
• Coverage improved significantly

---

🎉 Summary

This PR represents high-quality test engineering work. The minor suggestions above are truly optional - the code is production-ready as-is.

Recommendation: Merge after CI passes.

Key Strengths:

1. 🎯 Achieves stated coverage goals (96% harbor.py, 80% benchmark.py)
2. 📚 Excellent documentation of test strategy
3. 🔒 Proper security practices
4. 🏗️ Well-structured test organization

Optional improvements for future PRs:

• Consider adding 1-2 integration tests
• Extract some duplicate test patterns
• Add more edge case coverage for platform-specific behavior

---

Reviewed by: AgentReady Code Review Agent
Review Date: 2026-01-16
AgentReady Version: 2.23.0

Great work! 🚀

[github-actions]

AgentReady Code Review - PR #266

Overview

This PR adds comprehensive test coverage for CLI commands (benchmark and harbor), increasing project coverage from 64.5% to 67.0% (+2.5pp). The PR includes 50 new unit tests across two test modules.

---

✅ Strengths

1. Excellent Test Coverage Improvement

• cli/harbor.py: 20% → 96% (+76pp)
• cli/benchmark.py: 20% → 80% (+60pp)
• All 50 tests pass with comprehensive module documentation

2. High-Quality Test Design

• Proper isolation: Uses Click's CliRunner with isolated filesystem
• Type safety: Uses actual data models (HarborComparison, HarborRunMetrics, HarborTaskResult)
• Comprehensive fixtures: Well-documented fixtures for mocking complex dependencies
• Edge case coverage: Tests success paths, error handling, validation, and failure modes

3. Clear Documentation

• Each test module has a detailed docstring explaining:
  • Test strategy
  • Coverage targets
  • Test fixtures and their purposes
  • Special testing considerations (e.g., directory creation behavior)

4. Proper Mocking Strategy

• Mocks external dependencies (_real_tbench_result, HarborRunner, parse_harbor_results)
• Tests both high-level commands and internal helper functions independently
• Uses MagicMock appropriately for complex service objects

5. Test Organization

• Tests are organized into logical classes by functionality:
  • TestBenchmarkCommand, TestRunTbench, TestValidateAssessorCommand
  • TestRunBenchmarkPhase, TestGenerateReports, TestCreateLatestSymlinks, TestCompareCommand
• Each test method has a clear, descriptive name indicating what it tests

6. AgentReady Attribute Alignment

✅ Test Coverage (Tier 1): Significant improvement with 50 new tests
✅ Code Structure (Tier 2): Well-organized test modules with clear separation of concerns
✅ Documentation (Tier 1): Excellent test module documentation and inline comments
✅ .gitignore Updates (Tier 2): Properly excludes coverage.json and .claude/settings.local.json

---

🔍 Areas for Improvement

1. Security Considerations (Minor)

Issue: API key validation tests use mock environment variables but don't validate key format

@patch.dict("os.environ", {"ANTHROPIC_API_KEY": "test-key"})

Recommendation: Consider adding a test that validates the behavior when an invalid format API key is provided (not just missing). Real-world security best practice would be to validate API key format before use.

Severity: Low (testing code only, not production)

---

2. Test Data Realism (Minor)

Issue: Mock data uses fixed timestamps and simple values

created_at="2024-01-01T12:00:00",  # Fixed timestamp for test determinism

Recommendation: While deterministic timestamps are good for tests, consider using freezegun or similar libraries to make time-sensitive tests more explicit about time manipulation. This makes tests more maintainable when debugging time-related issues.

Severity: Low (current approach is acceptable)

---

3. Error Message Assertions (Minor)

Issue: Some tests verify exit codes but don't assert on specific error messages

assert result.exit_code != 0

Recommendation: Add assertions for expected error messages to catch regressions in error handling UX:

assert result.exit_code != 0
assert "specific error message" in result.output

Example: Tests like test_compare_no_tasks_specified do this well:

assert "At least one task must be specified" in result.output

Apply this pattern more consistently across all error tests.

---

4. Missing Test Coverage Gaps (Medium)

Based on the 80% coverage for benchmark.py and 96% for harbor.py, there are likely some edge cases not covered:

Potential gaps in benchmark.py (20% uncovered):

• Error handling for corrupted Harbor configuration
• Output directory creation edge cases (permissions, disk full)
• Verbose mode output validation
• Trajectory file handling when path is None or invalid

Potential gaps in harbor.py (4% uncovered):

• Symlink creation failure handling on Windows (line 120 has a pass statement)
• Exception details in _run_benchmark_phase error handling
• Browser opening failure in --open-dashboard flag

Recommendation:

1. Run pytest --cov=src/agentready/cli --cov-report=html --cov-report=term-missing to identify exact uncovered lines
2. Add tests for those specific lines or add comments explaining why they're intentionally untested

---

5. Test Fixture Duplication (Minor)

Issue: mock_comparison fixture is duplicated between test files with identical implementation (lines 98-122 in both files)

Recommendation: Consider extracting shared fixtures to a conftest.py file:

# tests/unit/conftest.py
@pytest.fixture
def mock_comparison():
    """Create mock Harbor comparison for testing."""
    # Implementation here

This follows the DRY principle and makes fixture maintenance easier.

---

6. Assertion Specificity (Minor)

Issue: Some tests use loose assertions

assert mock_run.assert_called_once()  # Line 138

Recommendation: Use more specific assertions to verify exact arguments:

mock_run.assert_called_once_with(
    repo_path=expected_path,
    subset="smoketest",
    # ... other expected args
)

---

🔒 Security Assessment

Overall: ✅ No security vulnerabilities detected

• No hardcoded credentials or secrets
• API key handling is properly mocked in tests
• No command injection risks in test code
• File path operations use Path objects correctly
• No unsafe deserialization of untrusted data

Minor observations:

• Tests properly mock ANTHROPIC_API_KEY environment variable
• No test creates security-sensitive files in production locations
• Temporary directories are properly cleaned up by fixtures

---

🏗️ Code Quality Assessment

Strengths:

• ✅ Follows Python testing best practices
• ✅ Proper use of pytest fixtures and parametrization opportunities
• ✅ Clear test names following test_<function>_<scenario> convention
• ✅ Comprehensive docstrings at module and fixture level
• ✅ Uses unittest.mock correctly for external dependencies
• ✅ Tests are independent and can run in any order

Minor improvements:

• Consider using pytest.mark.parametrize for tests with similar structure (e.g., different model selections, different subsets)
• Some assertion messages could be more descriptive for debugging failures

---

📋 Best Practices Compliance

✅ Followed:

• Type Safety: Uses actual data models from agentready.models.harbor
• Isolation: Tests don't depend on external services or file system state
• Reproducibility: Fixed timestamps and deterministic mock data
• Documentation: Excellent module-level and fixture-level docstrings
• Error Handling: Tests cover both success and failure paths

🔄 Could Improve:

• Test Parametrization: Some test methods could be parametrized to reduce duplication
• Fixture Sharing: Extract common fixtures to conftest.py
• Coverage Completeness: Push both modules to 90%+ coverage

---

🎯 Recommendations

Priority 1 (Before Merge):

None - PR is ready to merge as-is

Priority 2 (Follow-up PR):

1. Extract duplicate fixtures to conftest.py
2. Add specific assertions for error messages in all error-case tests
3. Run coverage report with --cov-report=term-missing and document or test remaining gaps

Priority 3 (Future Enhancement):

1. Consider using pytest-mock for cleaner mock syntax
2. Add integration tests for end-to-end CLI workflows
3. Consider using freezegun for time-sensitive tests

---

📊 Final Score

Category	Score	Notes
Test Coverage	95/100	Excellent improvement (+76pp harbor, +60pp benchmark)
Code Quality	90/100	Well-structured, clear, maintainable tests
Security	100/100	No vulnerabilities, proper mocking of sensitive data
Documentation	95/100	Excellent module docs, could add more inline comments
Best Practices	90/100	Follows testing best practices, minor DRY opportunities

Overall: ✅ Approved - High-quality test PR with excellent coverage improvements

---

🏆 Conclusion

This is a high-quality PR that significantly improves test coverage for critical CLI commands. The tests are well-designed, properly isolated, and comprehensive. The minor suggestions above are opportunities for incremental improvement, not blockers.

Recommendation: ✅ Merge

The PR successfully achieves its goals:

• ✅ Increases project coverage by 2.5pp (64.5% → 67.0%)
• ✅ Brings cli/harbor.py to 96% coverage (+76pp)
• ✅ Brings cli/benchmark.py to 80% coverage (+60pp)
• ✅ All 50 tests pass
• ✅ Proper .gitignore updates for coverage artifacts

Great work on this comprehensive test suite! 🎉

[github-actions]

AgentReady Code Review - PR #266

Overview

Comprehensive test coverage addition for benchmark and harbor CLI commands. The PR adds 50 new unit tests, increasing project coverage from 64.5% to 67.0% (+2.5pp), with significant improvements to critical CLI modules.

AgentReady Attribute Compliance Analysis

✅ Strengths (Attributes Well-Addressed)

1. Test Coverage (Tier 1 - Essential) ⭐

• Score Impact: Major positive contribution
• Evidence:
  • Project coverage: 64.5% → 67.0% (+2.5pp)
  • cli/harbor.py: 20% → 96% (+76pp)
  • cli/benchmark.py: 20% → 80% (+60pp)
  • 50 comprehensive tests with excellent documentation
• Assessment: Exceptional improvement to test coverage attribute

2. Code Documentation (Tier 1 - Essential) ⭐

• Evidence: Excellent module-level docstrings in both test files
  • test_cli_benchmark.py:1-25: Comprehensive test strategy documentation
  • test_cli_harbor.py:1-21: Clear coverage targets and fixture descriptions
• Best Practice: Documents test strategy, coverage targets, fixtures, and design decisions

3. Type Annotations (Tier 2 - Critical) ✓

• Evidence: Uses actual data models for type safety
  • HarborComparison, HarborRunMetrics, HarborTaskResult (lines 40-44)
  • Mock fixtures properly typed (test_cli_harbor.py:68-94)
• Assessment: Good use of type-safe data models instead of bare dicts

4. Error Handling (Tier 2 - Critical) ✓

• Evidence: Comprehensive error case coverage
  • Missing API keys: test_cli_benchmark.py:305-323, test_cli_harbor.py:528-537
  • Invalid inputs: test_cli_benchmark.py:289-303
  • Exception handling: test_cli_benchmark.py:353-369
  • Parse failures: test_cli_harbor.py:552-579
• Assessment: Thorough testing of error conditions and edge cases

5. Security Best Practices ✓

• Evidence: Safe environment variable handling
  • Uses @patch.dict for thread-safe mocking
  • Uses @patch.dict("os.environ", {}, clear=True) for missing key tests
• Best Practice: Thread-safe mocking instead of manual os.environ manipulation

📋 Code Quality Assessment

Strengths

1. Excellent test organization: Clear class structure with descriptive names
2. Comprehensive coverage: Success paths, error handling, and edge cases
3. Good fixture design: Reusable, well-documented fixtures
4. Deterministic tests: Fixed timestamps for reproducibility
5. Proper isolation: Uses CliRunner with isolated filesystem
6. Clear assertions: Meaningful assertion messages and checks

🔒 Security Review

✅ No Security Issues Found

1. API Key Handling: Properly mocked, never hardcoded
2. File System Operations: Uses temporary directories and isolated filesystems
3. Path Validation: Uses Click's path validation
4. No Command Injection: All commands properly mocked
5. No Sensitive Data: Test data is sanitized and generic

🏗️ Architectural Alignment

Follows AgentReady Best Practices:

• ✅ Library-first architecture (no global state)
• ✅ Strategy pattern for assessors (independent test classes)
• ✅ Fail gracefully (error tests verify graceful failures)
• ✅ User-focused (tests verify user-facing error messages)

Testing Best Practices:

• ✅ Unit tests for pure functions
• ✅ Integration tests for CLI commands
• ✅ Mock external dependencies (Harbor, file system)
• ✅ Use actual data models for type safety

📊 Impact on AgentReady Self-Assessment

Current Score: 80.0/100 (Gold)

Estimated Impact: +1 to +2 points

• Test Coverage attribute: Significant improvement (64.5% → 67.0%)
• Code Documentation attribute: Enhanced with excellent test documentation
• Type Annotations attribute: Maintained high quality in tests

New Estimated Score: 81-82/100 (Gold maintained)

✅ Final Verdict

EXCELLENT CONTRIBUTION - Already merged ✓

Summary:

• ✅ Significantly improves test coverage (+2.5pp overall, +76pp harbor, +60pp benchmark)
• ✅ Excellent code documentation and test organization
• ✅ No security vulnerabilities
• ✅ Follows AgentReady best practices
• ✅ High-quality, maintainable test code
• ✅ Proper error handling and edge case coverage

AgentReady Certification Impact: Maintains Gold status (80/100), slight score increase expected

Recommendation: APPROVED ✅

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Review generated using AgentReady code review analysis
Focus areas: AgentReady attributes, security, code quality, best practices