ProductHow it worksPricingBlogDocsLoginFind Your First Bug
Open-source AI self-driving software testing diagram.
Open SourceSelf driving testingAI

Introducing Open-Source AI Self-Driving Testing

Eugenio Scafati
Eugenio ScafatiCEO at Autonoma

Autonoma was built with a single conviction: there is too much waste and technical debt in software development, and most of it can be eliminated with AI.

Today, we are convinced that the biggest source of that waste is verification: making sure your code works and that you're not shipping regressions. AI has transformed how we write code, but we're still testing with legacy tools and brute-force human effort, and that gap is only widening.

Why existing approaches fall short

Traditional frameworks like Playwright, Cypress, and Appium are brittle, noisy, and difficult to maintain at scale.

AI prompting and click-and-record tools (including the previous version of Autonoma) require extensive setup, break easily, require proper test infrastructure in place and can't adapt to code changes by generating new coverage automatically.

Browser agents and MCP-based tools can exercise individual features, but they don't deliver full application coverage, they don't manage test users or environments, and they leave you uncertain about what's actually being tested and how.

None of these were designed for the way software is built today, therefore we believe a comprehensive, AI-native solution needs to exist, and that's what we're building.

We call it AI self-driving testing.

Why "self-driving"?

Because Autonoma handles everything required to build and maintain a testing platform, end to end:

  • Test user generation and management: automatically adapting as your database schema evolves.
  • Test plan generation, editing, deprecation, and execution: scaling with your codebase as it grows.
  • Test preview environments: provisioned as your infrastructure develops.
  • Test infrastructure to run tests in parallel: scalable Web browsers, Android emulators and iOS simulators.

Four-stage pipeline: test users and data, test plans and execution, preview environments, and parallel browsers and mobile simulators

Why open source?

We want to build for the developer community and establish the standard for AI-native development. We believe that requires three commitments:

  • Quality for everyone: AI democratized writing code, we're democratizing shipping it with confidence.
  • Your code, our code: Autonoma's engine reads your codebase, you should be able to read ours.
  • Ship it your way: No lock-in, deploy it, extend it and fork it as you want.

Try it today

We just shipped our alpha, you can generate and run test cases for free, starting now. We're shipping updates daily and working toward this mission in the open.

We'd love for you to try it out, contribute, and help spread the word. Access our code repository on GitHub, and our landing page.

Thank you,

Related articles

Reference architecture for self-hosted E2E testing platform showing CI to runner to browser pool flow inside a customer VPC

Self-Hosted E2E Testing Platform: A 2026 Guide

Self-hosted E2E testing in 2026: runner placement, ephemeral environments, optional on-prem LLM inference, and data residency. Architecture guide + comparison.

Diagram showing a wall of AI-generated pull requests overwhelming a small hand-maintained test suite, with a codebase-aware regression layer intercepting the merge flow

Regression Testing for AI-Generated Code: How to Keep Coverage Current When Agents Ship 100x More PRs

Regression testing AI-generated code: why Playwright suites collapse under agent PR volume and how codebase-aware AI code regression coverage survives drift.

AI E2E testing taxonomy: AI-assisted authoring, autonomous codebase-first testing, runtime exploration, natural-language spec execution, generated test pipelines, visual-AI assertions

AI E2E Testing: What It Actually Means in 2026

AI E2E testing covers six structurally different products: AI-assisted authoring, autonomous codebase-first testing, runtime exploration, natural-language spec execution, generated test pipelines, and visual-AI assertions. Only one is genuinely autonomous end to end.

Three-mechanism self-healing test automation taxonomy diagram contrasting locator-weighting, visual-diff, and intent re-derivation approaches.

AI Self-Healing Test Automation: Beyond Locator Fallback

Self-healing test automation has three mechanisms: locator-weighting, visual-diff, and intent re-derivation. See which one your vendor actually ships.