Web Application Codebase Audit: Identify Technical Debt

You switched to a new framework, but the codebase remains from the old team. Tests are failing, the build is growing, and every change triggers a regression. Sound familiar? We have encountered this dozens of times and know how to systematically measure technical debt (see Wikipedia) rather than guess. Suppose your project is on Laravel + Vue, and each deployment you wait 40 minutes for tests to pass, with half of them failing due to flaky environments. This is a typical symptom of an unhealthy codebase. Our codebase audit covers code quality, application architecture, code security, and performance optimization. Our Laravel audit includes PHPStan and Clockwork, and our TypeScript analysis uses strict mode and ESLint. Request an audit and get a transparent picture of your project's state. Eliminating technical debt reduces development time for new features by 30–50%, as confirmed by practical cases—clients report an average savings of $20,000 in development costs after removing technical debt.

Importance of Codebase Audit

The codebase is an asset that either works for you or slows you down. Without regular audits, problems accumulate: architectural mismatches, outdated dependencies, gaps in test coverage. According to Google's research, code is read 10 times more often than it is written — so code cleanliness directly impacts development speed. An audit provides an objective metric of project health and points where to focus efforts.

Audit Process

We cover all layers of the application: from static analysis to architectural connections. We don't just list problems — we rank them by severity and deliver a ready action plan. Typically, 20–30 issues are identified with 5–10 critical ones. Our clients report a 40% reduction in bug rates after implementing our recommendations.

Static Analysis

# TypeScript: strict checks (includes TypeScript analysis)
npx tsc --noEmit --strict

# ESLint: code quality analysis
npx eslint . --ext .ts,.tsx --format=json > eslint-report.json

# Dead code detection
npx ts-prune  # unused exports
npx knip     # unused dependencies and files

# Code duplicates
npx jscpd --min-tokens 50 --reporters html src/

Dependencies and Vulnerabilities

# npm audit (checks dependency vulnerabilities)
npm audit --json > audit-report.json

# Outdated packages
npm outdated --json

# Bundle size analysis
npx @next/bundle-analyzer
# Or
npx webpack-bundle-analyzer stats.json

# License checks (GPL risks in commercial projects)
npx license-checker --summary --onlyAllow "MIT;ISC;BSD;Apache-2.0"

Test Coverage

# Jest
npx jest --coverage --coverageReporters=json-summary

# Coverage thresholds in jest.config.ts:
coverageThreshold: {
  global: {
    branches: 60,
    functions: 70,
    lines: 70,
    statements: 70,
  },
},

Architectural Analysis

// PROBLEM: God Component (does everything)
// 500+ lines, multiple useEffect, direct API calls in the component

// PROBLEM: Prop drilling through 4+ levels
<App user={user}>
  <Layout user={user}>
    <Sidebar user={user}>
      <UserMenu user={user} />  // better: Context or Zustand

// PROBLEM: Circular dependencies
// utils → services → utils (can cause subtle bugs)
npx madge --circular src/

// PROBLEM: Large files
find src -name "*.ts" -o -name "*.tsx" | xargs wc -l | sort -n | tail -20

PHP/Laravel Audit

# PHPStan: static analysis
./vendor/bin/phpstan analyse --level=5 app/

# PHP Insights
php artisan insights

# N+1 query detection (Clockwork, Laravel Debugbar)
# config/debugbar.php: 'capture_ajax' => true

# Unused routes
php artisan route:list --format=json | python3 -c "
import json, sys
routes = json.load(sys.stdin)
print(f'Total routes: {len(routes)}')
"

Typical Mistakes We Find

Real-world case: a project on Laravel + React with 20 microservices. We discovered a circular dependency in the service container that caused memory leaks on every request. Fixing it reduced API response time by 40%. Here's what we encounter most often:

Report Compilation

The report is automatically assembled from the results of all tools. We add context: each problem is rated on a scale of 1 to 5 for severity and effort. For example, a circular dependency at the application level is rated as 3 (important but not urgent) with an estimated 8 hours for refactoring. The output includes a roadmap grouped into sprints.

Deliverables

You receive:

• A detailed report describing each problem, with code examples and a recommended fix.
• A prioritized task list with effort estimates (in hours) for each item.
• A refactoring roadmap covering 3–6 sprints.
• A 30-minute consultation session to explain the report and answer questions.
• Raw data access (JSON reports from tools) for your CI/CD pipeline.
• Code quality documentation to guide future development.
• Ongoing support for initial refactoring efforts.

Why Choose Us for Your Codebase Audit?

We have been working with web applications for many years and have audited 50+ projects, ranging from 10,000 to 500,000 lines of code. Our engineers are seasoned practitioners who write code daily in TypeScript, React, Laravel. We guarantee code confidentiality and a clear action plan. Static analysis is 10 times faster than manual review — we save your time. Get a consultation now.

Duration of the Audit

Average project (50–200 files) — 3–7 business days. Large codebases (500+ files) — 2–4 weeks. The timeline depends on complexity and analysis depth. We provide an accurate estimate after studying your project.

How to Get Started

1. Submit a request on our website — we'll contact you within a day.
2. Tell us about your project: stack, code volume, current pain points.
3. We propose an audit format and a preliminary estimate.
4. After agreement, we conduct the analysis in 3–14 days.
5. You receive a report with priorities and a roadmap. Investment typically starts at $2,500 for small projects and can go up to $10,000 for large codebases. Contact us to get a consultation and a preliminary estimate for your project.

Why are unit tests important but not a panacea?

A bug found by a unit test costs minutes to fix. The same bug in production costs hours of incident response, compensations, and lost trust. In an online store project, a discount calculation error passed manual testing, went to production, and processed 37 orders at zero price in 4 hours. An automated test for edge cases would have caught it on the first push. With 7+ years in web application testing and over 200 projects delivered, we’ve seen this pattern repeat across industries.

Jest is the standard for JavaScript/TypeScript, but unit tests are justified only where there is isolated logic: transformation functions, validators, business rules, utilities. Testing React components with Jest + Testing Library is correct for behavioral tests: "button appears after loading", "form shows error on empty email". Snapshot tests (toMatchSnapshot) are a trap: they break on any layout change and become noise that developers update without looking. Code coverage is a poor quality metric: 80% coverage can be achieved with tests that check nothing. Coverage shows that code executed, not that it works correctly.

Vitest as an alternative to Jest for Vite projects: 10–20x faster due to native ES modules without Babel transformation. For monorepos with thousands of tests, the speed difference is noticeable. Wikipedia on unit testing describes the theoretical foundation — we apply it with real CI pipelines.

How to set up E2E tests that are not flaky?

Playwright outperforms Cypress on key parameters: native multi-tab, multi-origin, iframe support; parallel execution at test level; WebKit, Firefox, Chromium out of the box; no iframe for the app — tests run in a real browser.

Playwright codegen records actions and generates a test — a good starting point, but generated code needs refactoring. Locators by text content are fragile: getByRole('button', { name: 'Place order' }) is more robust than locator('.btn-primary').

Page Object Model is the standard for organizing E2E tests. Each page is a separate class with methods instead of direct locators. When a button moves from header to sidebar — change in one place, not across all tests.

Flaky tests typically arise from race conditions between request and render, animations without wait, and dependency on external APIs. Solution: page.waitForResponse() instead of page.waitForTimeout(), mocking external APIs via page.route().

// Bad
await page.click('#submit');
await page.waitForTimeout(2000);
await expect(page.locator('.success')).toBeVisible();

// Good
await page.click('#submit');
await page.waitForResponse(resp =>
  resp.url().includes('/api/orders') && resp.status() === 201
);
await expect(page.getByRole('alert', { name: /order created/i })).toBeVisible();

Our engineers guarantee test stability in CI. Playwright’s official documentation covers all API details — we use it daily on projects with millions of users.

How do Core Web Vitals affect ranking?

Google uses Core Web Vitals in ranking. Lighthouse CLI in CI pipeline: on every deploy we check that LCP < 2.5s, CLS < 0.1, INP < 200ms. Google Chrome study: 53% of users leave a site if it takes longer than 3 seconds to load — our tests prevent such losses.

Real problems that Lighthouse finds:

• Hero image without width/height attributes: CLS 0.35 on load.
• JavaScript bundle 2.1MB synchronously blocking parsing: INP 450ms.
• Fonts without font-display: swap: invisible text until font loads (FOIT).
• Unoptimized hero image 4MB: LCP 8.2s.

Lighthouse CI (lhci) saves metric history and posts a comment to PR with degradation. For one e‑commerce client, optimizing these metrics improved conversion by 18% and reduced server costs by $12k annually.

What does load testing solve?

k6 is a load testing tool with a JavaScript API. Scenarios are written as code, versioned in git, run in CI. Three main scenarios:

• Spike test — sharp load increase: 0 → 1000 users in 30 seconds. Simulates a campaign launch. Shows system's ability to handle spikes.
• Soak test — stable load for 2–4 hours. Detects memory leaks, connection pool exhaustion, performance degradation.
• Stress test — load above expected (150–200% of peak). Shows breaking point and graceful degradation.

Thresholds:

thresholds: {
  http_req_duration: ['p95<500', 'p99<1000'],
  http_req_failed: ['rate<0.01'],
}

p95 < 500ms means 95% of requests respond faster than half a second. If threshold is not met, k6 exits with error code, CI pipeline fails.

In one online store project, we detected API degradation at the 4th hour of the test: p95 increased from 200ms to 2s due to connection leaks. After optimization, the client saved $15k per year on incident response and extra infrastructure.

Testing pyramid in a project

What does the work include?

• Audit of current coverage and identification of critical user flows.
• Writing unit tests for key business logic, integration tests for API, E2E for user scenarios.
• Setting up parallel execution in CI (sharded workers for Playwright).
• Load testing with report and recommendations.
• Test case documentation, training your team on test practices.
• 1-month warranty support after implementation.
• Delivery of all test artefacts (code, CI configs, run histories).

How do we work?

1. Analysis — audit of current testing, identification of weak spots, priority setting.
2. Design — tool selection, test plan writing, approval.
3. Implementation — writing tests, CI integration.
4. Testing — running all levels, result analysis, bug fixing.
5. Deployment — going live, metric monitoring, team training.

Timeline

Setting up a full test pipeline (Jest + Playwright + k6 + Lighthouse CI) from scratch: 2–4 weeks. E2E test coverage of an existing project (20–30 scenarios): 3–6 weeks. Load testing with report and recommendations: 1–2 weeks. Cost calculated individually after audit.

Ready to discuss your project? Leave a request — we will audit your current web application testing for free and propose a plan that can save up to 60% on incident costs. Get a consultation on web application testing — contact us today.