How I Combine Classical QA and AI-Era Testing for Modern Applications?

Today, AI isn’t just something we build for AI projects — it’s something we use in almost every modern application. From recommendation systems in e-commerce to chatbots in customer support, AI components are everywhere.

As a developer, I’ve realized that my testing strategy has to cover both classical QA (like unit tests, integration tests, security, performance) and AI-specific challenges (like data quality, model fairness, explainability, and drift detection).

In this post, I’m sharing my personal QAI checklist — a practical guide I use to test applications that combine traditional software with AI features. I’ll walk through the tools and practices I rely on across frontend, backend (Node.js, Python, PHP), and machine learning components, and explain how I set up automation, monitoring, and human feedback loops. 🔥

Introduction

We’ve entered a new era of software development where AI isn’t just limited to specialized projects — it’s baked into almost everything we build. Whether it’s a search feature, a recommendation engine, a chatbot, or a predictive dashboard, AI is becoming a standard part of the stack.

But here’s the challenge I’ve run into: classical QA approaches just aren’t enough anymore. Unit tests, integration tests, performance checks, and security audits are still essential — but they don’t tell me if the data behind my model is drifting, if my chatbot is giving biased answers, or if my recommendations are still relevant.

That’s why I’ve drafted a QAI checklist that helps systematically test both the classical layers and the AI components of modern applications.

In this blog post, I’ll walk through how I approach testing across the stack, share my favorite tools, and highlight the extra steps I take to make sure my applications are reliable, fair, and scalable in the AI age.

1. Why Modern QA Needs Both Classical and AI Testing

In today’s software, AI is no longer optional or limited to special projects. It’s part of search, chat, recommendations, fraud detection, personalization, predictive analytics — across almost every industry.

But here’s the problem I’ve learned from experience:
✅ Classical QA (unit, integration, E2E, performance tests) ensures the app works as coded.
❌ It does NOT guarantee the AI inside behaves safely, fairly, or reliably over time.

1.2 Classical QA — What It Catches

Classical QA is still absolutely necessary.
Here’s what I apply in all projects, with specific tools:

• Unit tests
  • Backend → Jest (Node.js), PyTest (Python), PHPUnit (PHP)
  • Frontend → Jest + Testing Library (React, Angular, Vue)
• Integration tests
  • Supertest (Node.js APIs), Postman/Newman collections, pytest-django (Python APIs)
• E2E tests
  • Playwright, Cypress (browser E2E)
  • Selenium, Puppeteer (cross-browser, visual checks)
• Performance/load tests
  • k6, Artillery, Locust (simulate high load, API stress testing)
• Security tests
  • OWASP ZAP, Snyk, npm audit, Bandit (Python static scan)

✅ Example: I test my Node.js backend API with Supertest + Jest for correct responses and failure cases.

1.3 New QA Problems Introduced by AI

When I add AI🤖 components (ML models, embeddings, LLMs, recommendation engines), new risk categories appear:

• Bias and fairness
  • Example: Product recommendations that favor popular items and ignore niche categories, harming small vendors.
  • Tool → IBM AI Fairness 360, Fairlearn
• Model drift
  • Example: A fraud detection model trained in 2023 becomes useless in 2025 as fraud patterns change.
  • Tool → Evidently AI (drift detection, dashboards)
• Explainability
  • Example: A customer support chatbot gives strange answers, and no one on the team understands why.
  • Tool → SHAP, LIME, Captum
• Data quality
  • Example: Missing fields in input data cause models to make garbage predictions.
  • Tool → Great Expectations (data validation)
• Adversarial robustness
  • Example: A vision model classifies a manipulated image incorrectly.
  • Tool → Adversarial Robustness Toolbox (ART)

1.4 How I Combine Classical QA with + QAI

1.5 Classical Frontend QA (still essential)

• Unit tests → Jest, Vitest, Mocha
• Integration tests → React Testing Library, Angular Testing Library
• E2E tests → Cypress, Playwright
• Visual regression → Percy, Chromatic, Loki
• Accessibility tests → axe-core, Lighthouse
• Cross-browser testing → BrowserStack, Sauce Labs

1.6 QAI (AI-aware Frontend Testing)

Because AI assists in coding, we face new risks:

✅ Code quality + readability check

• Use ESLint, Prettier → catch messy, inconsistent AI code
• Add CI checks for formatting + linting

✅ UI/UX consistency

• Review if AI-generated UI matches design systems (Storybook helps!)
• Use visual regression tests to detect style/layout drifts

✅ Hallucination + logic validation

• Write unit/integration tests for AI-generated helpers

Example:

// Watch for overcomplicated helpers:
const getUniqueList = arr => [...new Set(arr)];

✅ Dependency + version correctness

• Check library versions and updates
• Use npm audit, pnpm audit, Snyk to spot insecure or outdated packages

✅ Hidden complexity / dead code detection

• Detect unused imports, vars, functions
• Use webpack, Rollup, ESLint no-unused-vars

✅ Fallback behavior testing

• Simulate API failures and timeouts
• Ensure user-friendly fallbacks Example:

describe('Fallback UI', () => {
  it('shows fallback when AI API fails', async () => {
    server.use(
      rest.get('/api/ai-endpoint', (req, res, ctx) =>
        res(ctx.status(500))
      )
    );
    render(<RecommendationWidget />);
    expect(await screen.findByText('Sorry, recommendations unavailable')).toBeInTheDocument();
  });
});

✅ Security + injection check

• Audit AI-generated string interpolations + HTML handling

✅ Performance testing

• Use Lighthouse, WebPageTest to catch inefficiencies
• Watch for missed debouncing/throttling

2. Frontend QA + QAI

When using AI in frontend development, several risks can arise, and they must be considered during testing.

These risks are a mix of classic QA issues and unique QAI-specific problems that AI tools may introduce:

2.1.1 Code Quality and Readability

AI-generated code might not always follow best practices or your project’s coding standards, making it difficult to maintain or debug in the future.

Inconsistent Code: AI might generate code that lacks consistency with your project’s structure.

Unclear Variable Naming: AI might generate functions or variables with non-descriptive names, making the code harder to understand.

Exmaple:

// AI-generated code
function f(x, y) {
  return x * y;
}

Better practice would be:

function calculateProduct(x, y) {
  return x * y;
}

QAI Testing Solution: Automate checks for naming conventions and code structure consistency using linters and formatters like ESLint or Prettier.

2.1.2 Inconsistent UI/UX

AI might generate UI code that doesn’t align with the desired UI/UX principles, resulting in inconsistencies or poor accessibility.

Design Inconsistencies: AI might not use consistent design systems, resulting in mismatched colors, fonts, and component styles.

Accessibility: AI may neglect accessibility features such as ARIA roles or keyboard navigation.

Example:

<button>Click Me</button>

Better practice would be:

<button aria-label="Submit form">Click Me</button>

QAI Testing Solution: Use accessibility testing tools like axe-core or Lighthouse to automate the detection of accessibility issues and ensure that AI-generated code adheres to accessibility standards.

2.1.3 Security Vulnerabilities

AI might unintentionally introduce security risks by generating insecure code, especially around user inputs or API calls.

XSS Vulnerabilities: AI may fail to sanitize user inputs correctly, exposing your application to cross-site scripting (XSS).

Insecure API Calls: AI may generate API calls without HTTPS, exposing sensitive data to interception.

Example:

// AI-generated insecure API call
fetch('/api/user', { method: 'POST', body: JSON.stringify(user) })
  .then(response => response.json());

Better practice:

fetch('/api/user', {
  method: 'POST',
  headers: { 'Content-Type': 'application/json' },
  body: JSON.stringify(user),
  secure: true
}).then(response => response.json());

QAI Testing Solution: Use tools like OWASP ZAP or Snyk for security audits and automated vulnerability scanning to check for risks like XSS, SQL Injection, and more.

2.1.4 Performance Issues

AI tools can generate code that works but isn’t optimized for performance.

Inefficient Rendering: AI might fail to consider rendering performance, resulting in unnecessary DOM manipulations or re-renders.

Unoptimized Bundles: AI-generated code may not take advantage of lazy loading or code splitting, causing larger bundle sizes and slower load times.

Example:

// AI-generated without debouncing
inputElement.addEventListener('input', function() {
  renderResults();
});

Better practice:

const debouncedRender = debounce(() => renderResults(), 300);
inputElement.addEventListener('input', debouncedRender);

QAI Testing Solution: Leverage performance tools like WebPageTest or Lighthouse to measure rendering times, bundle sizes, and resource loading times. Regularly check for performance bottlenecks caused by AI-generated code.

2.1.5 Versioning and Dependency Management

AI tools might suggest outdated or incompatible libraries, leading to dependency issues.

Outdated Libraries: AI might suggest outdated versions of libraries or frameworks, resulting in compatibility issues.

Dependency Conflicts: AI-generated code may introduce conflicts with the current versions of libraries in your project.

QAI Testing Solution: Use Dependabot or similar tools to automatically check for outdated dependencies and version conflicts. Ensure that your package.json is up-to-date and compatible with your project’s requirements.

2.1.6 Unpredictable Behavior and Lack of Transparency

AI-generated code can sometimes lead to unpredictable behavior due to the lack of context or transparency in the code generation process.

Hidden Logic: AI might introduce complex or non-obvious solutions that could lead to bugs.

Contextual Errors: AI may fail to grasp the full context of the project, causing issues in certain scenarios or edge cases.

QAI Testing Solution: Conduct manual reviews alongside unit testing to identify potential edge cases that might arise from AI-generated code. Automated testing frameworks like Jest, Mocha, or Cypress can help in identifying unexpected behaviors.

2.2.1 Mitigating the Risks of AI in Frontend Development

To reduce these risks, developers should implement specific strategies, such as:

Code Reviews: AI-generated code should be thoroughly reviewed by developers to ensure quality and consistency.

Automated Testing: Build comprehensive unit, integration, and end-to-end tests to catch issues early and ensure the frontend behaves as expected.

Performance Testing: Use performance tools like Lighthouse or WebPageTest to continuously monitor rendering speeds and resource usage.

Security Audits: Use automated security tools to detect potential vulnerabilities in AI-generated code.

Manual and Automated Checks: Use linting and formatting tools (e.g., ESLint, Prettier) to enforce best practices, and run accessibility checks through tools like axe-core.

Summary

By combining traditional frontend QA practices with QAI-specific strategies, we can effectively ensure that AI-assisted frontend development doesn’t compromise quality, security, performance, or user experience. It’s essential to balance the convenience of AI tools with careful testing and validation to ensure robust and reliable frontend applications.