from datetime import datetime, timedelta
from unittest.mock import patch
import pytest
from boring.intelligence.brain_manager import BrainManager, GlobalKnowledgeStore, LearnedPattern
from boring.services.storage import _clear_thread_local_connection
@pytest.fixture
def temp_project(tmp_path):
project = tmp_path / "project"
project.mkdir()
yield project
_clear_thread_local_connection()
class TestBrainManagerEnhanced:
"""Enhanced tests for BrainManager (V10.23+ features)."""
def test_incremental_learn(self, temp_project):
brain = BrainManager(temp_project)
print(f"DEBUG: BrainManager methods: {[m for m in dir(brain) if not m.startswith('_')]}")
# Test basic incremental learning
result = brain.incremental_learn(
error_type="python_error",
error_message="KeyError: 'user'",
solution="Ensure user key exists in dictionary.",
file_path="src/auth.py",
)
assert result["status"] == "SUCCESS"
patterns = brain._load_patterns()
assert len(patterns) == 1
assert patterns[0]["pattern_type"] == "error_solution"
def test_pattern_relevance_decay(self, temp_project):
brain = BrainManager(temp_project)
# Create an old pattern
old_date = (datetime.now() - timedelta(days=100)).isoformat()
pattern = LearnedPattern(
pattern_id="OLD_1",
pattern_type="code_fix",
description="Old pattern",
context="old context",
solution="old fix",
success_count=1,
created_at=old_date,
last_used=old_date,
decay_score=1.0,
)
import dataclasses
brain._save_patterns([dataclasses.asdict(pattern)])
# Update decay
result = brain.update_pattern_decay()
assert result["updated"] == 1
# Reload and check score
patterns = brain._load_patterns()
assert patterns[0]["decay_score"] < 1.0
def test_prune_patterns(self, temp_project):
brain = BrainManager(temp_project)
# Add patterns via the official API
brain.learn_pattern("temp", "Low score pattern", "ctx", "sol")
# Verify pattern exists before pruning
initial_stats = brain.get_pattern_stats()
assert initial_stats["total"] >= 1
# Prune with high threshold - should remove patterns
result = brain.prune_patterns(min_score=0.99)
# Result should indicate pruning operation completed
assert "pruned_count" in result or "status" in result
def test_get_pattern_stats(self, temp_project):
brain = BrainManager(temp_project)
brain.learn_pattern("code_style", "Indent 4", "ctx", "sol")
brain.learn_pattern("error_solution", "Fix bug", "ctx2", "sol2")
stats = brain.get_pattern_stats()
assert stats["total"] == 2
assert stats["by_type"]["code_style"] == 1
assert stats["by_type"]["error_solution"] == 1
def test_global_knowledge_store_empty(self, temp_project):
with patch("pathlib.Path.home", return_value=temp_project):
store = GlobalKnowledgeStore()
assert store.list_global_patterns() == []
# Export from project
brain = BrainManager(temp_project / "proj")
brain.learn_pattern("test", "desc", "ctx", "sol")
# Set success count to 2 to pass default threshold
patterns = brain._load_patterns()
patterns[0]["success_count"] = 5
brain._save_patterns(patterns)
result = store.export_from_project(temp_project / "proj", min_success_count=2)
assert result["exported"] == 1
# Import to another project
result_import = store.import_to_project(temp_project / "proj2")
assert result_import["imported"] == 1
def test_brain_health_report(self, temp_project):
brain = BrainManager(temp_project)
brain.learn_pattern("error", "desc", "ctx", "sol")
report = brain.get_brain_health_report()
assert report["total_patterns"] == 1
assert "health_status" in report
def test_intelligent_pattern_match_no_words(self, temp_project):
brain = BrainManager(temp_project)
brain.learn_pattern("test", "desc", "ctx", "sol")
# Test with empty context
results = brain.get_relevant_patterns("")
assert len(results) == 1
