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Preparing Your Codebase for 2026: A Technical Debt Audit Guide

Before the new year begins, take time to audit your codebase. Here’s a systematic approach to identifying and prioritizing technical debt for 2026.

The Audit Framework

1. Dependency Health Check

# Check for outdated packages
dotnet list package --outdated

# Check for vulnerabilities
dotnet list package --vulnerable

# For Node.js
npm audit
npm outdated

Create a dependency report:

import subprocess
import json

def audit_dependencies():
    """Generate dependency health report."""
    report = {
        "outdated": [],
        "vulnerable": [],
        "deprecated": []
    }

    # Run npm audit
    result = subprocess.run(
        ["npm", "audit", "--json"],
        capture_output=True,
        text=True
    )
    audit_data = json.loads(result.stdout)

    for vuln_id, vuln in audit_data.get("vulnerabilities", {}).items():
        report["vulnerable"].append({
            "package": vuln_id,
            "severity": vuln["severity"],
            "recommendation": vuln.get("fixAvailable", "No fix available")
        })

    return report

2. Code Complexity Analysis

# Using radon for Python
radon cc src/ -a -s

# Using SonarQube for comprehensive analysis
sonar-scanner \
  -Dsonar.projectKey=my-project \
  -Dsonar.sources=src \
  -Dsonar.host.url=http://localhost:9000

Flag files exceeding thresholds:

from radon.complexity import cc_visit

def find_complex_functions(source_code: str, threshold: int = 10):
    """Find functions with cyclomatic complexity above threshold."""
    results = cc_visit(source_code)

    complex_functions = [
        {
            "name": r.name,
            "complexity": r.complexity,
            "line": r.lineno
        }
        for r in results
        if r.complexity > threshold
    ]

    return complex_functions

3. Test Coverage Gaps

# Generate coverage report
dotnet test --collect:"XPlat Code Coverage"

# Identify untested code
reportgenerator \
  -reports:coverage.cobertura.xml \
  -targetdir:coveragereport \
  -reporttypes:Html

Prioritize coverage for:

  • Business-critical paths
  • Recently changed code
  • Integration points

4. Documentation Debt

def audit_documentation(repo_path: str) -> dict:
    """Check documentation completeness."""
    issues = []

    # Check for README
    readme_path = os.path.join(repo_path, "README.md")
    if not os.path.exists(readme_path):
        issues.append("Missing README.md")

    # Check for API documentation
    api_docs = glob.glob(f"{repo_path}/**/openapi*.yaml", recursive=True)
    if not api_docs:
        issues.append("Missing OpenAPI specification")

    # Check for architecture docs
    arch_docs = glob.glob(f"{repo_path}/**/ARCHITECTURE.md", recursive=True)
    if not arch_docs:
        issues.append("Missing architecture documentation")

    return {
        "documentation_score": max(0, 100 - len(issues) * 20),
        "issues": issues
    }

5. Performance Bottlenecks

Review APM data for:

  • Slow database queries
  • High memory usage patterns
  • API endpoints with high latency
-- Find slow queries in Azure SQL
SELECT TOP 20
    qs.total_elapsed_time / qs.execution_count AS avg_elapsed_time,
    qs.execution_count,
    SUBSTRING(st.text, (qs.statement_start_offset/2)+1,
        ((CASE qs.statement_end_offset
            WHEN -1 THEN DATALENGTH(st.text)
            ELSE qs.statement_end_offset
        END - qs.statement_start_offset)/2)+1) AS query_text
FROM sys.dm_exec_query_stats qs
CROSS APPLY sys.dm_exec_sql_text(qs.sql_handle) st
ORDER BY avg_elapsed_time DESC;

Prioritization Matrix

CategoryImpactEffortPriority
Security vulnerabilitiesHighVariesP0
Performance bottlenecksHighMediumP1
Outdated frameworksMediumHighP1
Test coverage gapsMediumMediumP2
Code complexityLowHighP3
DocumentationLowLowP3

Create Your 2026 Roadmap

  1. Q1: Address all P0 and critical P1 items
  2. Q2: Complete remaining P1 items
  3. Q3: Work through P2 items
  4. Q4: Continuous P3 improvements

Technical debt compounds like financial debt. Regular audits and systematic paydown keep your codebase healthy and your team productive.

Michael John Peña

Michael John Peña

Senior Data Engineer based in Sydney. Writing about data, cloud, and technology.