Advanced Software Composition Analysis
Advanced Software Composition Analysis
Modern SCA tools go beyond simple vulnerability databases. They analyze dependency behavior, identify suspicious patterns, and predict future risks. Machine learning models trained on package history can identify dependencies likely to become abandoned or compromised. This predictive analysis helps teams make better dependency choices.
#!/usr/bin/env python3
# advanced_sca_analyzer.py - Advanced dependency analysis
import json
import requests
from datetime import datetime, timedelta
from typing import Dict, List, Any
import networkx as nx
from collections import defaultdict
import numpy as np
from sklearn.ensemble import RandomForestClassifier
class AdvancedDependencyAnalyzer:
def __init__(self):
self.vulnerability_db = VulnerabilityDatabase()
self.package_metrics = PackageMetricsCollector()
self.risk_model = self._load_risk_model()
def analyze_dependency_tree(self, manifest_path: str) -> Dict[str, Any]:
"""Perform comprehensive dependency analysis"""
# Build dependency graph
dep_graph = self._build_dependency_graph(manifest_path)
# Analyze each package
package_risks = {}
for package in dep_graph.nodes():
risk_assessment = self._assess_package_risk(package)
package_risks[package] = risk_assessment
# Analyze dependency relationships
relationship_risks = self._analyze_relationships(dep_graph)
# Calculate aggregate risk metrics
aggregate_metrics = self._calculate_aggregate_risk(
package_risks,
relationship_risks
)
return {
'package_risks': package_risks,
'relationship_risks': relationship_risks,
'aggregate_metrics': aggregate_metrics,
'recommendations': self._generate_recommendations(package_risks)
}
def _assess_package_risk(self, package: str) -> Dict[str, Any]:
"""Comprehensive risk assessment for a package"""
package_name, version = package.split('@')
# Direct vulnerability assessment
vulnerabilities = self.vulnerability_db.get_vulnerabilities(
package_name, version
)
# Package health metrics
health_metrics = self.package_metrics.collect_metrics(package_name)
# Predictive risk scoring
risk_features = self._extract_risk_features(health_metrics)
predicted_risk = self.risk_model.predict_proba([risk_features])[0][1]
# License compliance check
license_risk = self._assess_license_risk(package_name)
# Supply chain risk factors
supply_chain_risk = self._assess_supply_chain_risk(package_name)
return {
'vulnerabilities': vulnerabilities,
'health_score': self._calculate_health_score(health_metrics),
'predicted_risk': predicted_risk,
'license_risk': license_risk,
'supply_chain_risk': supply_chain_risk,
'overall_risk': self._calculate_overall_risk(
vulnerabilities, predicted_risk, license_risk, supply_chain_risk
)
}
def _analyze_relationships(self, dep_graph: nx.DiGraph) -> Dict[str, Any]:
"""Analyze dependency relationship risks"""
risks = {
'circular_dependencies': list(nx.simple_cycles(dep_graph)),
'single_points_of_failure': self._find_critical_packages(dep_graph),
'deep_dependency_chains': self._find_deep_chains(dep_graph),
'version_conflicts': self._detect_version_conflicts(dep_graph)
}
# Calculate relationship complexity metrics
risks['complexity_metrics'] = {
'average_degree': np.mean([d for n, d in dep_graph.degree()]),
'clustering_coefficient': nx.average_clustering(dep_graph.to_undirected()),
'longest_path': len(nx.dag_longest_path(dep_graph))
}
return risks
def _extract_risk_features(self, health_metrics: Dict) -> List[float]:
"""Extract features for risk prediction model"""
features = [
health_metrics.get('days_since_last_release', 999),
health_metrics.get('monthly_downloads', 0),
health_metrics.get('github_stars', 0),
health_metrics.get('open_issues_count', 0),
health_metrics.get('contributor_count', 0),
health_metrics.get('commit_frequency', 0),
health_metrics.get('has_security_policy', 0),
health_metrics.get('uses_semver', 0),
health_metrics.get('test_coverage', 0),
health_metrics.get('documentation_score', 0)
]
return features
def _calculate_health_score(self, metrics: Dict) -> float:
"""Calculate overall package health score"""
weights = {
'maintenance_activity': 0.25,
'community_size': 0.20,
'code_quality': 0.20,
'security_practices': 0.20,
'documentation': 0.15
}
scores = {
'maintenance_activity': self._score_maintenance(metrics),
'community_size': self._score_community(metrics),
'code_quality': self._score_quality(metrics),
'security_practices': self._score_security(metrics),
'documentation': self._score_documentation(metrics)
}
return sum(scores[k] * weights[k] for k in weights)
class DependencyPolicyEngine:
"""Policy engine for dependency management"""
def __init__(self, policy_file: str):
self.policies = self._load_policies(policy_file)
def evaluate_dependencies(self, dependencies: List[Dict]) -> Dict[str, Any]:
"""Evaluate dependencies against policies"""
violations = []
warnings = []
for dep in dependencies:
for policy in self.policies:
result = self._evaluate_policy(policy, dep)
if result['violated']:
if policy['severity'] == 'error':
violations.append({
'dependency': dep['name'],
'policy': policy['name'],
'message': result['message']
})
else:
warnings.append({
'dependency': dep['name'],
'policy': policy['name'],
'message': result['message']
})
return {
'passed': len(violations) == 0,
'violations': violations,
'warnings': warnings
}
def _evaluate_policy(self, policy: Dict, dependency: Dict) -> Dict:
"""Evaluate single policy against dependency"""
if policy['type'] == 'vulnerability':
return self._check_vulnerability_policy(policy, dependency)
elif policy['type'] == 'license':
return self._check_license_policy(policy, dependency)
elif policy['type'] == 'age':
return self._check_age_policy(policy, dependency)
elif policy['type'] == 'source':
return self._check_source_policy(policy, dependency)
else:
return {'violated': False}
# Dependency update automation
class DependencyUpdateAutomation:
"""Automated dependency update management"""
def __init__(self, repo_config: Dict):
self.repo = repo_config
self.update_strategies = {
'security': SecurityFirstStrategy(),
'conservative': ConservativeStrategy(),
'aggressive': AggressiveStrategy()
}
def plan_updates(self, strategy: str = 'security') -> List[Dict]:
"""Plan dependency updates based on strategy"""
current_deps = self._get_current_dependencies()
available_updates = self._check_available_updates(current_deps)
strategy_impl = self.update_strategies[strategy]
update_plan = strategy_impl.plan(current_deps, available_updates)
# Validate update plan
validation_results = self._validate_update_plan(update_plan)
return {
'updates': update_plan,
'validation': validation_results,
'estimated_risk': self._estimate_update_risk(update_plan)
}
def execute_updates(self, update_plan: List[Dict], dry_run: bool = True):
"""Execute dependency updates with safety checks"""
results = []
for update in update_plan:
if dry_run:
result = self._simulate_update(update)
else:
result = self._perform_update(update)
results.append(result)
# Stop on critical failures
if result['status'] == 'failed' and result['critical']:
break
return results