# 3 Ways I Use AI to Auto-Generate Playwright Locators

> Source: <https://dev.to/abhishek_jain_cd78ebe598d/-3-ways-i-use-ai-to-auto-generate-playwright-locators-278c>
> Published: 2026-09-12 21:27:42+00:00

If you've maintained a Playwright suite for more than a few months, you know the real cost isn't writing tests — it's the constant locator rot. A designer tweaks a class name, a component gets refactored, and suddenly a dozen tests are red for reasons that have nothing to do with actual bugs.

After 18 years in test automation — including building enterprise-scale frameworks — I've found that AI-assisted locator generation is one of the highest-leverage places to bring LLMs into a QA workflow. Here are three approaches I actually use, not just demo-ware.

Instead of hand-picking a `data-testid` or fighting brittle CSS selectors, I feed a snapshot of the relevant DOM section to an LLM and ask it to propose the most resilient locator strategy — prioritizing accessible roles and text over implementation-specific attributes.

``` js
// Instead of guessing at a selector manually:
const button = page.locator('.btn.btn-primary.mt-2.submit-btn-v2');

// Ask an AI-assisted step to suggest something resilient:
const button = page.getByRole('button', { name: 'Submit Order' });
```

The win isn't that AI "knows" your app — it's that it consistently nudges you toward Playwright's built-in resilient locator patterns (`getByRole`, `getByLabel`, `getByText`) instead of the CSS-selector habits many teams fall back into under deadline pressure.

When a primary locator fails during a run, rather than failing the test outright, I use an AI step to analyze the current DOM and suggest a fallback locator that matches the *original intent* of the interaction — then logs the drift so a human confirms it later.

This isn't "magic self-healing" that silently rewrites your test suite (which I'd actually caution against — silent healing can mask real regressions). It's a flagged suggestion: "Your original locator broke; here's what looks like the same element now; confirm before I update the source."

For teams writing BDD-style scenarios ("User clicks the 'Add to Cart' button"), I use AI to translate that natural language directly into a first-draft Playwright locator + action, which a human then reviews and commits.

```
When the user clicks "Add to Cart"
```

becomes a suggested:

```
await page.getByRole('button', { name: 'Add to Cart' }).click();
```

This dramatically speeds up onboarding less-experienced testers into a Playwright framework, since they're writing intent, not fighting Playwright's API surface on day one.

**The common thread across all three:** AI isn't replacing test design judgment — it's removing the tedious, error-prone parts of locator selection so testers can focus on *what* to test, not *how* to write a selector.

I go deeper into all of this — plus patterns for AI-assisted test generation, flaky test triage, and building a production-grade framework end to end — in my book, **[Playwright Test Automation with AI](https://kdp.amazon.com/amazon-dp-action/us/dualbookshelf.marketplacelink/B0HJLR1W6Y)**.