feat: Add comprehensive tests for report summary engine #63
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WalkthroughAdds a new test module Changes
Sequence Diagram(s)No sequence diagram — changes are test additions only and do not modify or introduce control-flow between components. Estimated code review effort🎯 2 (Simple) | ⏱️ ~10 minutes
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🧹 Nitpick comments (4)
backend/app/services/summary/tests/test_summary_engine_advanced.py (4)
19-21: Usepytest.approx()for floating point comparisons.Exact equality checks for floating point numbers can lead to flaky tests due to precision issues.
Apply this diff:
- assert scores["tokenomics_strength"] == (0.6 + 0.5) / 2 * 10 # 0.5 is default for utility_score - assert scores["sentiment_health"] == (0.8 - 0.1 + 1) / 2 * 10 - assert scores["code_maturity"] == (0.8 * 0.6 + (1 - 0.1) * 0.4) * 10 # 0.1 is default for bug_density + assert scores["tokenomics_strength"] == pytest.approx((0.6 + 0.5) / 2 * 10) + assert scores["sentiment_health"] == pytest.approx((0.8 - 0.1 + 1) / 2 * 10) + assert scores["code_maturity"] == pytest.approx((0.8 * 0.6 + (1 - 0.1) * 0.4) * 10)
9-22: Add assertions for all computed scores.The test only verifies 3 of 5 scores returned by
generate_scores. Add assertions foraudit_confidenceandteam_credibilityto ensure complete validation of default fallback behavior.Add these assertions:
assert scores["audit_confidence"] == pytest.approx(min(1 * 2, 5) + 1.0 * 5) # defaults: num_audits=1, critical_resolved=1.0 assert scores["team_credibility"] == pytest.approx((0.7 * 0.5 + 0.8 * 0.5) * 10) # defaults: experience=0.7, transparency=0.8
34-38: Usepytest.approx()for floating point comparisons.Same issue as the previous test—exact equality for floating point numbers should be avoided.
Apply this diff:
- assert scores["tokenomics_strength"] == (0.0 + 1.0) / 2 * 10 - assert scores["sentiment_health"] == (1.0 - 0.0 + 1) / 2 * 10 - assert scores["code_maturity"] == (0.0 * 0.6 + (1 - 1.0) * 0.4) * 10 - assert scores["audit_confidence"] == min(0 * 2, 5) + 0.0 * 5 - assert scores["team_credibility"] == (0.0 * 0.5 + 1.0 * 0.5) * 10 + assert scores["tokenomics_strength"] == pytest.approx((0.0 + 1.0) / 2 * 10) + assert scores["sentiment_health"] == pytest.approx((1.0 - 0.0 + 1) / 2 * 10) + assert scores["code_maturity"] == pytest.approx((0.0 * 0.6 + (1 - 1.0) * 0.4) * 10) + assert scores["audit_confidence"] == pytest.approx(min(0 * 2, 5) + 0.0 * 5) + assert scores["team_credibility"] == pytest.approx((0.0 * 0.5 + 1.0 * 0.5) * 10)
1-141: Consider adding tests for missing data categories and edge boundaries.For more comprehensive coverage, consider adding:
- A test for
generate_scoreswhen entire data categories are missing (e.g., noaudit_dataorteam_datakeys), to verify all defaults work correctly.- A test for
build_final_summarywith a score exactly at5.0to explicitly verify the weakness/neutral boundary behavior.Example test for missing categories:
def test_generate_scores_missing_categories(summary_engine): # Test with completely missing data categories data = {} scores = summary_engine.generate_scores(data) # All scores should use defaults assert "tokenomics_strength" in scores assert "sentiment_health" in scores assert "code_maturity" in scores assert "audit_confidence" in scores assert "team_credibility" in scores
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backend/app/services/summary/tests/test_summary_engine_advanced.py (1)
backend/app/services/summary/report_summary_engine.py (1)
ReportSummaryEngine(4-72)
🔇 Additional comments (4)
backend/app/services/summary/tests/test_summary_engine_advanced.py (4)
1-6: LGTM!Standard pytest setup with a clean fixture definition.
41-68: LGTM!Test correctly validates the all-strengths scenario with appropriate assertions for structure, counts, and specific items.
69-96: LGTM!Test correctly validates the all-weaknesses scenario with appropriate assertions.
97-141: LGTM!Comprehensive test that validates mixed scores, including the edge case of
7.0at the strength boundary. The assertions for overall summary content and scores dictionary are thorough and correct.
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🧹 Nitpick comments (3)
backend/app/services/summary/tests/test_summary_engine_advanced.py (3)
28-28: Consider removing the unusedlines_of_codeparameter.The
lines_of_codevalue is provided in the test data but is not used in the actual score calculation (as seen in the production code). While it doesn't affect test correctness, removing it would make the test data clearer and avoid potential confusion about which fields actually influence the calculations.
47-49: Replace hard-coded values with explicit calculations.Hard-coding expected values like
7.8and7.0makes the test brittle and obscures the relationship to the production code's default values. If defaults change in the production code, the test failure won't clearly indicate which assumption broke.Apply this diff to make the expectations explicit and maintainable:
- assert scores["code_maturity"] == pytest.approx(7.8) # defaults: test_coverage=0.7, bug_density=0.1 - assert scores["audit_confidence"] == pytest.approx(min(1 * 2, 5) + 1.0 * 5) # defaults: num_audits=1, critical_resolved=1.0 - assert scores["team_credibility"] == pytest.approx((0.7 * 0.5 + 0.8 * 0.5) * 10) # defaults: experience=0.7, transparency=0.8 + assert scores["code_maturity"] == pytest.approx((0.7 * 0.6 + (1 - 0.1) * 0.4) * 10) # defaults: test_coverage=0.7, bug_density=0.1 + assert scores["audit_confidence"] == pytest.approx(min(1 * 2, 5) + 1.0 * 5) # defaults: num_audits=1, critical_resolved=1.0 + assert scores["team_credibility"] == pytest.approx((0.7 * 0.5 + 0.8 * 0.5) * 10) # defaults: experience=0.7, transparency=0.8
51-62: Consider removing this duplicate test.This test is redundant with
test_generate_scores_default_fallbacks(lines 40-50). Both tests pass an emptydatadict and verify the same default behavior. The earlier test already validates that all score keys are present (implicitly through value assertions), making this test unnecessary.
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backend/app/services/summary/tests/test_summary_engine_advanced.py (1)
backend/app/services/summary/report_summary_engine.py (1)
ReportSummaryEngine(4-72)
🔇 Additional comments (4)
backend/app/services/summary/tests/test_summary_engine_advanced.py (4)
1-6: LGTM!The imports and fixture setup are correct and follow pytest conventions.
9-22: LGTM!This test correctly validates the fallback behavior when partial data is provided, using the default values from the production code.
64-91: LGTM!This test thoroughly validates the all-strengths scenario, correctly asserting that scores >= 7.0 are classified as strengths according to the production code logic.
92-197: LGTM!All three remaining test functions provide excellent coverage:
test_build_final_summary_all_weaknessesvalidates the all-weaknesses scenariotest_build_final_summary_mixed_scoresthoroughly tests mixed classifications with detailed assertions on contenttest_build_final_summary_boundary_score_5_0correctly validates the boundary case where scores of exactly 5.0 fall into neither strengths nor weaknesses
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Actionable comments posted: 1
🧹 Nitpick comments (1)
backend/app/services/summary/tests/test_summary_engine_advanced.py (1)
8-185: Consider adding error handling and edge case tests.While the current test coverage is solid for happy-path and boundary scenarios, consider adding tests for:
- Invalid input types (e.g., strings instead of numbers in data/scores)
- Negative values or scores > 10
- Empty
nlg_outputsdict with valid scores- Malformed data structures
These additional tests would improve robustness and catch potential runtime errors.
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🔇 Additional comments (6)
backend/app/services/summary/tests/test_summary_engine_advanced.py (6)
23-39: Good boundary value coverage.The test effectively covers boundary values (0.0 and 1.0) for all scoring components. The
audit_confidencecalculation with zero audits correctly produces a score of 0, which appropriately represents projects with no audit history.
108-153: Comprehensive mixed-score test coverage.This test effectively validates the classification logic for strengths (≥7), weaknesses (<5), and neutral scores (5-6). It also verifies the overall_summary content formatting and score mapping structure.
154-185: Excellent boundary case test for neutral threshold.This test thoroughly validates that scores exactly at 5.0 are classified as neutral (neither strength nor weakness), with explicit assertions for each score category.
1-6: Import path and module structure are correct.The
ReportSummaryEngineclass exists atbackend/app/services/summary/report_summary_engine.py, and all required__init__.pyfiles are present in the package hierarchy. The import statement is valid and will resolve correctly.
74-78: ****The test assertions correctly verify the key mappings. The
build_final_summaryimplementation transforms snake_case score keys (e.g.,tokenomics_strength) to TitleCase (e.g.,"Tokenomics Strength") using.replace('_', ' ').title(), which matches all assertions in lines 74-78, 102-106, 130-131, and 134-135. The strength/weakness thresholds (≥7.0 and <5.0, respectively) are also correctly implemented and tested.
19-21: All scoring formulas and default values are correct.Verification confirms the test assertions in lines 19-21, 34-38, and 45-49 all match the
ReportSummaryEngine.generate_scoresimplementation exactly. The tested formulas and defaults (distribution/utility: 0.5; positive/negative ratio: 0.5; test_coverage: 0.7; bug_density: 0.1; num_audits: 1; critical_resolved: 1.0; experience: 0.7; transparency: 0.8) all align with the implementation. The tests appropriately verify partial data fallback to defaults, boundary conditions, and complete default scenarios.
| data = { | ||
| "tokenomics_data": {"distribution_score": 0.6}, # Missing utility_score | ||
| "sentiment_data": {"positive_sentiment_ratio": 0.8, "negative_sentiment_ratio": 0.1}, | ||
| "code_audit_data": {"test_coverage": 0.8}, # Missing lines_of_code, bug_density |
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Clarify the comment about missing fields.
The comment mentions "lines_of_code" as a missing field, but this field is not referenced in any of the test assertions. If lines_of_code is not used in the scoring formula, remove it from the comment to avoid confusion.
- "code_audit_data": {"test_coverage": 0.8}, # Missing lines_of_code, bug_density
+ "code_audit_data": {"test_coverage": 0.8}, # Missing bug_density📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| "code_audit_data": {"test_coverage": 0.8}, # Missing lines_of_code, bug_density | |
| "code_audit_data": {"test_coverage": 0.8}, # Missing bug_density |
🤖 Prompt for AI Agents
In backend/app/services/summary/tests/test_summary_engine_advanced.py around
line 14, the inline comment references "lines_of_code" as a missing field
although no test assertions or scoring logic reference it; either remove
"lines_of_code" from the comment to avoid confusion or, if it should be part of
the test, add the field to the test input and corresponding assertions/score
usage. Update the comment to only mention actual missing fields (e.g.,
"bug_density") or implement the field in the test to match the comment.
|
Nice, the added tests will definitely boost the reliability of the summary engine. 🚀 Approved! |
Overview: This PR introduces a new suite of tests for the report summary engine, ensuring the accuracy and reliability of its core functionalities.
Changes
backend/app/services/summary/tests/and utilize example data from mock agents for thorough validation.Summary by CodeRabbit
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