File size: 12,594 Bytes
9df97a2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 | """
Comprehensive integration tests for Phase 1 IA enhancements.
Tests integration of:
1. Adaptive Thresholds
2. Smart Deduplication
3. Explainability Engine
4. Smart Fallback Responder
5. Skill Quality Analyzer
"""
import json
import sys
from pathlib import Path
# Add backend to path
sys.path.insert(0, str(Path(__file__).parent))
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker, Session
from app.models.models import Base, Candidate, Skill, CandidateSkill, JobCriteria, CriteriaSkill, MatchResult, ProficiencyLevel
from app.services.matching_engine import (
score_candidate_against_criteria,
build_skill_universe,
get_adaptive_thresholds,
generate_enriched_explanation,
extract_candidate_skill_names,
)
# ============================================================================
# TEST FIXTURES
# ============================================================================
def setup_test_db() -> Session:
"""Create in-memory SQLite database for testing."""
engine = create_engine("sqlite:///:memory:")
Base.metadata.create_all(engine)
SessionLocal = sessionmaker(bind=engine)
return SessionLocal()
def create_test_skills(db: Session) -> dict:
"""Create test skill records."""
skills_data = {
"Python": "python",
"React": "react",
"TypeScript": "typescript",
"Docker": "docker",
"AWS": "aws",
"SQL": "sql",
}
skills = {}
for name, _ in skills_data.items():
skill = Skill(name=name, category="tech")
db.add(skill)
db.flush()
skills[name] = skill
db.commit()
return skills
def create_test_candidate(db: Session, name: str, skills: list) -> Candidate:
"""Create test candidate with skills."""
candidate = Candidate(
full_name=name,
email=f"{name.lower()}@test.com",
raw_text=f"{name} CV with skills: {', '.join(skills)}",
is_fully_extracted=True,
extraction_quality_score=95,
)
db.add(candidate)
db.flush()
# Add skills
for skill_name in skills:
skill = db.query(Skill).filter(Skill.name.ilike(skill_name)).first()
if not skill:
skill = Skill(name=skill_name, category="tech")
db.add(skill)
db.flush()
cs = CandidateSkill(
candidate_id=candidate.id,
skill_id=skill.id,
proficiency_level=ProficiencyLevel.intermediate,
source="test",
)
db.add(cs)
db.commit()
return candidate
def create_test_criteria(db: Session, title: str, required_skills: dict) -> JobCriteria:
"""Create test job criteria."""
criteria = JobCriteria(
recruiter_id=1,
title=title,
description=f"Test criteria for {title}",
)
db.add(criteria)
db.flush()
# Add required skills
for skill_name, weight in required_skills.items():
skill = db.query(Skill).filter(Skill.name.ilike(skill_name)).first()
if not skill:
skill = Skill(name=skill_name, category="tech")
db.add(skill)
db.flush()
cs = CriteriaSkill(
criteria_id=criteria.id,
skill_id=skill.id,
weight=weight
)
db.add(cs)
db.commit()
return criteria
# ============================================================================
# TEST SUITES
# ============================================================================
def test_adaptive_thresholds():
"""Test adaptive threshold engine integration."""
print("\nπ Testing Adaptive Thresholds...")
# Test default case
thresholds = get_adaptive_thresholds()
assert "accept" in thresholds, "Missing accept threshold"
assert "review" in thresholds, "Missing review threshold"
assert thresholds["accept"] > thresholds["review"], "Accept threshold should be higher"
print(" β
Default thresholds: PASS")
# Test domain-specific thresholds
data_scientist_thresholds = get_adaptive_thresholds("Senior Data Scientist")
assert data_scientist_thresholds is not None, "Should return thresholds for data scientist"
print(f" β
Data Scientist thresholds: accept={data_scientist_thresholds.get('accept')}, review={data_scientist_thresholds.get('review')}")
# Test another domain
frontend_thresholds = get_adaptive_thresholds("Senior React Developer")
assert frontend_thresholds is not None, "Should return thresholds for frontend"
print(f" β
Frontend thresholds: accept={frontend_thresholds.get('accept')}, review={frontend_thresholds.get('review')}")
print(" β
Adaptive Thresholds: ALL TESTS PASSED")
def test_smart_dedup():
"""Test smart deduplication integration."""
print("\nπ Testing Smart Deduplication...")
db = setup_test_db()
create_test_skills(db)
# Create candidate with duplicate skills
candidate = create_test_candidate(
db, "John Doe",
["Python", "React", "python", "PYTHON", "react", "TypeScript"]
)
# Extract skills - should be deduplicated
extracted_skills = extract_candidate_skill_names(candidate)
print(f" Extracted skills: {extracted_skills}")
# Check deduplication worked
python_count = sum(1 for s in extracted_skills if s.lower() == "python")
react_count = sum(1 for s in extracted_skills if s.lower() == "react")
assert python_count == 1, f"Python should appear once, found {python_count}"
assert react_count == 1, f"React should appear once, found {react_count}"
assert "TypeScript" in extracted_skills, "TypeScript should be present"
print(" β
Deduplication removed duplicates correctly")
print(" β
Smart Deduplication: ALL TESTS PASSED")
def test_explainability_engine():
"""Test explainability engine integration."""
print("\nπ Testing Explainability Engine...")
db = setup_test_db()
create_test_skills(db)
# Create test data
candidate = create_test_candidate(db, "Alice", ["Python", "React", "Docker"])
criteria = create_test_criteria(
db, "Senior React Developer",
{"Python": 40, "React": 50, "TypeScript": 10}
)
# Get criteria skills for enriched explanation
criteria_skills_models = db.query(CriteriaSkill).filter(
CriteriaSkill.criteria_id == criteria.id
).all()
criteria_skills = [
{"name": cs.skill.name, "weight": cs.weight}
for cs in criteria_skills_models
]
# Score candidate
score, details = score_candidate_against_criteria(candidate, criteria_skills)
print(f" Candidate score: {score}%")
# Generate enriched explanation
enriched = generate_enriched_explanation(candidate, score, details, criteria_skills)
assert "score" in enriched, "Should have score in explanation"
assert "matched_skills" in enriched, "Should have matched_skills"
assert "missing_skills" in enriched, "Should have missing_skills"
print(f" β
Explanation generated: {enriched.get('summary', 'N/A')}")
print(" β
Explainability Engine: ALL TESTS PASSED")
def test_smart_fallback():
"""Test smart fallback responder integration."""
print("\n㪠Testing Smart Fallback...")
try:
from ai_module.chatbot.smart_fallback import SmartFallbackResponder
db = setup_test_db()
create_test_skills(db)
candidate = create_test_candidate(db, "Bob", ["Python", "Docker"])
criteria = create_test_criteria(
db, "Python Developer",
{"Python": 60, "Docker": 40}
)
responder = SmartFallbackResponder()
# Test method availability
assert hasattr(responder, 'explain_score_fallback'), "Missing explain_score_fallback"
assert hasattr(responder, 'compare_candidates_fallback'), "Missing compare_candidates_fallback"
assert hasattr(responder, 'greeting_fallback'), "Missing greeting_fallback"
print(" β
All SmartFallbackResponder methods present")
print(" β
Smart Fallback: ALL TESTS PASSED")
except ImportError:
print(" β οΈ Smart Fallback not available (dependencies missing)")
def test_skill_quality():
"""Test skill quality analyzer integration."""
print("\nβ Testing Skill Quality Analyzer...")
try:
from ai_module.matching.skill_quality import SkillQualityAnalyzer
db = setup_test_db()
create_test_skills(db)
# Create multiple candidates with various skills
create_test_candidate(db, "Candidate1", ["Python", "React", "Docker"])
create_test_candidate(db, "Candidate2", ["Python", "TypeScript"])
create_test_candidate(db, "Candidate3", ["React", "AWS", "SQL"])
analyzer = SkillQualityAnalyzer()
metrics = analyzer.compute_metrics(db)
assert "quality_score" in metrics, "Missing quality_score"
assert "total_skills" in metrics, "Missing total_skills"
assert "unique_skills" in metrics, "Missing unique_skills"
assert "health_status" in metrics, "Missing health_status"
print(f" Quality Score: {metrics.get('quality_score')}")
print(f" Total Skills: {metrics.get('total_skills')}")
print(f" Unique Skills: {metrics.get('unique_skills')}")
print(f" Health Status: {metrics.get('health_status')}")
print(" β
Skill Quality Analyzer: ALL TESTS PASSED")
except ImportError:
print(" β οΈ Skill Quality Analyzer not available (dependencies missing)")
def test_full_matching_pipeline():
"""Test complete matching pipeline with all Phase 1 features."""
print("\nπ Testing Full Matching Pipeline (End-to-End)...")
db = setup_test_db()
create_test_skills(db)
# Create candidates
candidates = [
create_test_candidate(db, "Alice", ["Python", "React", "Docker", "AWS"]),
create_test_candidate(db, "Bob", ["Python", "Django", "PostgreSQL"]),
create_test_candidate(db, "Charlie", ["JavaScript", "React", "Node.js"]),
]
# Create criteria
criteria = create_test_criteria(
db, "Senior Full Stack Developer",
{"Python": 30, "React": 40, "Docker": 20, "TypeScript": 10}
)
# Score all candidates
criteria_skills_models = db.query(CriteriaSkill).filter(
CriteriaSkill.criteria_id == criteria.id
).all()
criteria_skills = [
{"name": cs.skill.name, "weight": cs.weight}
for cs in criteria_skills_models
]
results = []
for candidate in candidates:
score, details = score_candidate_against_criteria(candidate, criteria_skills)
results.append({
"candidate": candidate.full_name,
"score": score,
"coverage": details.get("coverage"),
"matched": details.get("matched_skills"),
"missing": details.get("missing_skills"),
})
# Sort by score
results.sort(key=lambda x: x["score"], reverse=True)
print(" π Final Rankings:")
for i, result in enumerate(results, 1):
print(f" {i}. {result['candidate']}: {result['score']}% "
f"(Coverage: {result['coverage']}%, Matched: {len(result['matched'])})")
# Verify ranking makes sense
assert results[0]["score"] >= results[1]["score"], "Ranking should be by score"
print(" β
Full Matching Pipeline: ALL TESTS PASSED")
# ============================================================================
# MAIN EXECUTION
# ============================================================================
def main():
"""Run all tests."""
print("=" * 80)
print("PHASE 1 INTEGRATION TEST SUITE")
print("=" * 80)
try:
test_adaptive_thresholds()
test_smart_dedup()
test_explainability_engine()
test_smart_fallback()
test_skill_quality()
test_full_matching_pipeline()
print("\n" + "=" * 80)
print("β
ALL TESTS PASSED - PHASE 1 INTEGRATION SUCCESSFUL")
print("=" * 80)
return 0
except AssertionError as e:
print(f"\nβ TEST FAILED: {e}")
return 1
except Exception as e:
print(f"\nβ ERROR: {e}")
import traceback
traceback.print_exc()
return 1
if __name__ == "__main__":
sys.exit(main())
|