Forem: Holly Russell

From ChatGPT for Testing to Full Automation: Why I Built My Own AI Testing Tool

Holly Russell — Thu, 12 Feb 2026 09:26:47 +0000

I got tired of pasting DOM snippets, so I built a tool that uses video to generate Playwright scripts.
If you are anything like us, your browser history is likely a graveyard of Gemini conversations and ChatGPT threads named "Fix this Playwright Selector" or "Generate Test Script."

We have all tried using ChatGPT for testing. It usually goes like this: you open the dev tools, copy a massive chunk of your DOM, paste it into ChatGPT, and pray it understands which is actually the "Submit" button.

It works, but it is exhausting. It feels less like "full automation" and more like advanced copy-pasting.

We recently went down a rabbit hole looking for a better AI testing tool. We needed something that could take the logic of an LLM but apply it directly to the browser without the manual friction. When we couldn't find exactly what we needed, we teamed up with our co-founder, Sandeep, and the Bug0 crew to build it.

It is called Bug0 Studio (at vibe.bug0.com), and it has completely shifted how we generate Playwright scripts. It is the same core technology we use to serve our enterprise customers in our Forward Deployed Engineering (FDE) model in QA testing, so we knew it had to be robust.

Here is the story of why we built it, and why it has become the best AI software testing tool for our own workflow.

The Problem: Why Text Prompts Aren't Enough
The biggest limitation we faced when using ChatGPT for usability testing or script generation was blindness. An LLM (Large Language Model) doesn’t see your site; it only reads the text you feed it.

Sure, you can use ChatGPT for guerilla usability testing ideas. You can ask it to generate user personas or hypothetical scenarios. But when it comes to the actual execution, specifically writing the code that clicks the button, it often fails because it lacks context. It doesn't know that your "Login" button is actually hidden behind a cookie banner, or that your specific React component renders differently on mobile.

We wanted a workflow that felt like ChatGPT. It needed to be conversational and smart, but we needed it to have "eyes" on our application.

Enter Bug0 Studio: The "Vibe" Testing Approach
We designed Bug0 Studio as a vibe testing platform. The idea was straightforward: instead of writing code or messing with complex prompts, we simply wanted to show the AI the "vibe" of the user journey and have it generate the script.

We designed the workflow to be as simple as possible:

Record, Upload, or Type: We made it flexible - you can record your browser tab directly, upload a video file (mp4/webm), or just describe the flow in natural language text.

Describe: You tell it what you are testing in plain English.

Get Code: It processes the video recording along with your text description and spits out a fully working Playwright test.

We needed a real AI tool for automation testing that connects the user's view directly to the code logic. For teams looking to extend automation beyond QA, AI Work Models offer purpose-built AI solutions that streamline workflows, cut costs, and boost efficiency.

A screenshot of the Bug0 Studio landing page showing the "Record Tab" and "Upload Video" options side-by-side.

Bug0 Studio Screenshot: The entry point is simple: either record live or upload a video.

The "Lazy Developer's" Dream Setup
What we focused on most wasn't just the AI generation, but the specific configuration options. Most AI tools for testing require a lot of boilerplate setup. We tried to identify the exact pain points developers (including ourselves) have when setting up test suites.

When you start a session, we designed a configuration panel that handles the boring stuff:

Site Setup: You just drop in your Base URL.

Storage State JSON: This is the killer feature we knew we needed.

We are paranoid about security, so we made a specific design choice here. We designed the system so that your Base URL and credentials stay in localStorage on your machine. They aren't stored permanently on our servers, which is a huge relief.

A close-up screenshot of the Bug0 Studio configuration panel. Specifically highlighting the "Storage State JSON" field and the Login Credentials section.

Being able to paste a Playwright storage state JSON means you can test deep-link flows without re-logging in every time.

If you have ever written E2E tests for a dashboard behind a login wall, you know the pain. Usually, you have to script the login steps for every single test. With Bug0, you can just paste your Playwright storageState JSON, and the AI assumes that session. It effectively acts as an AI tool for manual testing logic that automates the setup for you.

A Practical Example
A simple checkout flow is the best way to show how we use it effectively. This is usually a nightmare for brittle selectors.

The Workflow:

We clicked "Record Tab" in Bug0 Studio.

We clicked on a product, added it to the cart, and proceeded to checkout.

We stopped the recording.

The Validation Step: We didn't want it to be a "black box," so we built an intermediate step. The AI analyzes the video and extracts a list of ordered steps. We can actually edit, add, or remove steps right there. This allows us to fix a logic error we made during recording before the AI writes any code.

We verified the steps and hit "Generate."

The Result: Once the generation started, the tool spun up a live cloud browser session. We watched a split-screen view: on the left, the AI was explaining its logic in real-time; on the right, we saw a live preview of the test actually running on the cloud browser.

It wasn't just "guessing" based on HTML; it was executing the "vibe" of the user journey in front of our eyes, ending with a generated Playwright script that used robust locators like getByRole.

A split-screen image. On the left, a frame of the video recording showing the checkout. On the right, the generated code block showing the Playwright script.

The Landscape: Why We Built This
When searching for an AI testing tool, we mostly found three types of solutions. Here is why we found them frustrating enough to build our own solution:

1. The Chatbots (ChatGPT, Claude)
We love them, but they are blind. You have to feed them HTML snapshots or screenshot uploads manually. If the DOM changes or uses dynamic classes (like CSS modules), the bot often hallucinates selectors that look correct but fail immediately.

Zero real-time visual context; requires constant manual copy-pasting.
**

The "Literal" Recorders (Playwright Codegen)** These tools are technically impressive, but they are literal to a fault. If we accidentally click the wrong field and then correct ourselves, the recorder scripts both actions. They also tend to default to specific hierarchical selectors (e.g., div:nth-child(3)) which break the moment we add a new UI element.

Brittle scripts. They record actions, not intent.

3. The "Black Box" Enterprise Suites
There are plenty of "Autonomous Testing Agents" out there. They promise self-healing and zero-code, but they usually require us to install their heavy SDKs, run tests on their specific cloud infrastructure, and pay enterprise pricing.

Vendor lock-in. We designed Bug0 Studio to just give us standard Playwright code that we can run in our own local VS Code or CI/CD pipeline.

Conclusion
We are seeing a flood of AI tools right now, and it is hard to tell which ones are actually useful. But if you are looking for a ChatGPT for testing alternative that is purpose-built for modern web apps, we invite you to check out what we've built.

It solves the context problem by using video, and it solves the setup problem by handling Auth and Storage States natively.

Give it a spin at Bug0 Studio. It’s surprisingly fun to just upload a video and watch clean code appear. We truly believe this is not just a temporary fix, but the blueprint for the best AI software testing tool of the future.

Feel free to hop on into our Discord and share your thoughts or feature requests.

Key Steps to Automation Every Developer Should Know

Holly Russell — Wed, 11 Feb 2026 10:02:48 +0000

From efficient code writing to comprehensive testing, deployment, and monitoring, automation empowers developers to focus on solving complex challenges rather than getting bogged down in mundane workflows. In this article, we’ll explore how automation is reshaping software development, making it faster, more reliable, and far less stressful.

**## 1. Automation in Development*
*

1.1 Automated Code Generation
Imagine a world where your code could practically write itself. With AI-powered tools like GitHub Copilot and OpenAI Codex, this is becoming a reality. These intelligent assistants suggest code snippets in real-time, significantly reducing development time and turning coding into a collaborative conversation rather than a chore.

Frameworks like Next.js and Magento come equipped with built-in automation capabilities that handle repetitive tasks such as routing, component generation, and data fetching, allowing developers to focus on higher-level functions instead of tedious, repetitive tasks.

1.2 Version Control & Collaboration Automation
Version control conflicts can be a nightmare for developers. Fortunately, tools like GitHub Actions and GitLab CI/CD automate version control processes, ensuring smooth collaboration across teams.

Automated code reviews, facilitated by tools like SonarQube, scan your codebase for potential issues before they escalate, leading to fewer bugs and a more robust codebase. As development teams begin connecting automated code generation, database workflows, and deployment processes end-to-end, frameworks like AI Work workflow models are increasingly used to design cohesive automation pipelines that span the entire development lifecycle

1.3 Database Management Automation
Writing database queries manually can be time-consuming and error-prone. Enter ORM (Object-Relational Mapping) tools like Sequelize, Hibernate, and Prisma, which automate database interactions, making data management more intuitive.

Automated database migrations also ensure data consistency across different environments, alleviating concerns about data integrity and synchronization.

***2. Automation in Testing*
**

2.1 Automated Unit Testing
Manual testing is a time-consuming and often frustrating process. Frameworks like Jest, Mocha, and PyTest streamline unit testing, allowing you to catch bugs early in the development cycle. Continuous testing ensures that your code remains reliable with every change, minimizing future debugging challenges.

2.2 UI & Functional Testing Automation
Launching an application only to discover critical UI issues can be catastrophic. UI and functional testing automation tools like Selenium, Cypress, and Playwright automate these processes, ensuring your application performs flawlessly across devices and browsers.

2.3 Performance & Security Testing
What happens when thousands of users hit your site simultaneously? Load testing tools like JMeter and Locust simulate high-traffic scenarios, helping you identify potential bottlenecks before they impact users.

On the security front, automated tools such as OWASP ZAP and Burp Suite help detect vulnerabilities early, providing a proactive defense against potential threats.

***3. Automation in Deployment & Monitoring*
**
**
3.1 Continuous Integration & Continuous Deployment (CI/CD)**
Deployment should be seamless, not stressful. CI/CD pipelines powered by Jenkins, GitHub Actions, and CircleCI automate the building, testing, and deployment processes for every code change. This allows teams to release updates confidently, knowing they won’t break existing functionality.

Feature flagging enables gradual feature rollouts, minimizing risk and maximizing stability.

3.2 Infrastructure as Code (IaC)
Manually setting up infrastructure can be cumbersome and error-prone. Infrastructure as Code (IaC) tools like Terraform and AWS CloudFormation automate these processes, ensuring consistency and reliability in your deployments.

Container orchestration tools like Docker and Kubernetes further streamline deployments, enabling scalable and efficient management of containerized applications.

3.3 Automated Monitoring & Alerts
What if your application experiences downtime in the middle of the night? Automated monit

oring tools like Prometheus, Datadog, and New Relic alert you to issues in real time, allowing for immediate response and remediation.

AI-powered anomaly detection tools can even preemptively identify potential problems, helping to maintain optimal performance and user experience

Automation testing is a must in AI world.

Holly Russell — Wed, 04 Feb 2026 06:48:10 +0000

Artificial Intelligence (AI) is transforming the way we work, whether through tools like GitHub Copilot, ChatGPT, or other AI‑powered development assistants. These tools help streamline our workflows, reduce manual effort, and accelerate application development.
In software testing, AI enhances accuracy and increases the reliability of end‑to‑end (E2E) testing by allowing teams to focus on real end‑user outcomes. Building AI‑driven E2E regression automation enables testing processes to become more efficient, consistent, and scalable—ultimately helping us deliver high‑quality, defect‑free software products.Just as AI-driven testing automates repetitive quality checks and streamlines workflows, AI Work models can handle recurring accounting and business processes, reducing manual effort and improving accuracy without requiring deep technical expertise.

Customers:
Users across mobile applications, web applications, and APIs

Pain Point:

1-End-to-end (E2E) testing is not being performed due to a lack of skilled QA resources.
2-Applications developed by the engineering team are not being fully tested across all expected scenarios.
3-AI‑driven applications require more thorough testing, including comprehensive positive and negative scenarios.
4-Slow test execution, is causing delays in production releases. Repetitive defects are being detected in production due to incomplete regression coverage.
5-High maintenance effort is required for automation test cases. Test data generation and proper utilization remain inconsistent and inefficient.
Overall testing efforts are resulting in low or no return on investment (ROI).
Actions needed for software testing with AI

1-Smart and rapid test case creation Self‑healing test scripts that automatically adapt to UI and API changes
2-Easy scaling and simplified maintenance of regression suites Automated test data generation using intelligent scripting Reduced code volume with no redundant or duplicate scripting Significant improvement in overall test coverage Unified API and UI testing capabilities
3-Intelligent test execution with priority‑based optimization Predictive defect analytics for early issue detection Write-once, execute-anywhere support (cross‑browser and cross‑platform)
4-Enhanced reporting with detailed insights for development and QA teams
Natural language–based test automation for faster authoring and collaboration.
Results

Fast implementation with minimal setup (Page Object Model, Behavior‑Driven, Test Data–Driven, Machine Learning–Driven, and AI‑Driven frameworks).
No requirement for deep internal AI expertise—any team member with basic programming knowledge can contribute.
Significantly reduced test maintenance effort that improves automatically over time.
Easy integration with CI/CD pipelines to support high‑quality, low‑defect releases.
Suitable for Agile, DevOps, or any modern software development methodology.

Conclusion

Major defects were identified early during upgrades through automated regression testing, well before reaching production.
Consecutive defect‑free functional releases, demonstrating the effectiveness of our automation strategy.

ABC Link Exchange: A Collaborative Approach to Growth

Holly Russell — Fri, 30 Jan 2026 07:13:03 +0000

Hello,

I’m reaching out to suggest a partnership through a link exchange.

This collaboration can increase our visibility and help both our sites succeed online.

I’ve enjoyed your work and believe we could support each other well.

If you’re interested, I’d love to discuss the details and see how we can collaborate!

Warm regards

Opportunity for Strategic Link Exchange

Holly Russell — Fri, 30 Jan 2026 07:11:17 +0000

Hello,

I’m reaching out to suggest a partnership through a link exchange.

This collaboration can [increase](https://www.idstrong.com/) our visibility and help both our sites succeed online.

I’ve enjoyed your work and believe we could support each other well.

If you’re interested, I’d love to discuss the details and see how we can collaborate!

Warm regards