microsoft/TypeAgent
Publicmirrored fromhttps://github.com/microsoft/TypeAgentAvailable
python/ta/spec/coverage.txt
57lines · modecode
| 1 | |
| 2 | ## Test coverage |
| 3 | |
| 4 | ## Analysis of Uncovered Code in semrefindex.py |
| 5 | Based on the coverage report, here are the main areas of semrefindex.py that are currently uncovered: |
| 6 | |
| 7 | 🔍 Major Uncovered Areas: |
| 8 | Knowledge Extraction Pipeline (lines 81-102) |
| 9 | |
| 10 | process_semantic_refs_batch() function |
| 11 | Batch processing of text through knowledge extractor |
| 12 | Error handling for knowledge extraction failures |
| 13 | Entity Processing (lines 173-197) |
| 14 | |
| 15 | add_entity() function - detailed entity processing |
| 16 | Adding entity types and facets as separate terms |
| 17 | add_facet() function for processing entity facets |
| 18 | Action Processing (lines 277-341) |
| 19 | |
| 20 | add_action() function - comprehensive action processing |
| 21 | Handling verbs, subject/object entities, parameters |
| 22 | Complex parameter processing (string vs object params) |
| 23 | Subject entity facet processing |
| 24 | Knowledge Integration (lines 369-409) |
| 25 | |
| 26 | add_knowledge_to_semantic_ref_index() function |
| 27 | Validation and integration of entities, actions, topics |
| 28 | Inverse action processing |
| 29 | Serialization/Deserialization (lines 508-521, 579, 616-621) |
| 30 | |
| 31 | TermToSemanticRefIndex serialization methods |
| 32 | Data persistence functionality |
| 33 | Advanced Index Operations (lines 468-474, 531, 551) |
| 34 | |
| 35 | Complex term preparation and scoring |
| 36 | Advanced term lookup and removal operations |
| 37 | Conversation Building (lines 631, 645-647) |
| 38 | |
| 39 | build_conversation_index() function |
| 40 | build_semantic_ref_index() function |
| 41 | Auto knowledge extraction settings |
| 42 | Utility Functions (lines 673-680) |
| 43 | |
| 44 | dump() function for debugging/inspection |
| 45 | 🎯 Coverage Improvement Opportunities: |
| 46 | To increase coverage, we would need tests that: |
| 47 | |
| 48 | Test knowledge extraction pipeline with actual knowledge extractor |
| 49 | Test complex entity processing with facets and multiple types |
| 50 | Test action processing with all parameter types and edge cases |
| 51 | Test serialization/deserialization of populated indexes |
| 52 | Test conversation building end-to-end scenarios |
| 53 | Test error handling in knowledge extraction failures |
| 54 | Test utility functions like dump() |
| 55 | The current 55% coverage suggests that while we have good basic functionality testing, we're missing tests for the more complex, real-world usage scenarios that involve knowledge extraction, complex data structures, and full conversation processing workflows. |
| 56 | |
| 57 | (ACTUALLY it's 64%, still low) |
| 58 | |