AI TESTING LAB

AI Testing Lab

Positioning: QA / SDET + AI Testing Engineer. Put LLMs into reusable test workflows — not demos.

Principle: AI accelerates drafts; a review loop decides what ships. Every direction must connect to real artifacts (cases, automation, reports).

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Shipped product

AI Test Case Generator

Live

Problem

When requirements arrive as PDFs, screenshots, or rough notes, turning them into scenarios and boundary cases is slow and hard to reuse.

Approach

Multimodal input → AI generation → scored Test / Dev / Product Manager review loop → export Excel / Markdown / XMind; runs via Docker / local source / npm / OpenClaw.

Input → Requirement

Test Scenarios
Test Cases
Boundary Cases
Negative Cases

Requirement
Multimodal parse
LLM generation
Review loop
Export / install

Open-source on GitHub
Docker image
Installable ClawHub skill
Case study with live install metrics

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Research directions

These are not casual buzzwords. They are the next methods on the same AI Testing map — exploring / roadmap only; no fake repositories.

AI API Testing

Exploring

Start from OpenAPI / contracts, let the LLM draft executable cases, then feed an existing API automation framework.

OpenAPI
LLM
Test Cases
API Automation

Target artifact: reviewable API suite drafts that harden into stable regression cases.

AI Bug Analysis

Exploring

Feed bug reports, logs, and stack traces to an LLM to produce checkable root-cause hypotheses and repro hints.

Bug Report
Logs + Stack Trace
LLM
Root Cause Analysis

Target artifact: structured RCA drafts that shorten triage — not replace engineer judgment.

AI Testing Agent

Roadmap

An agent that closes the loop from plan → cases → execution → defect detection → report on an observable pipeline.

Requirement
AI Agent
Test Plan
Test Cases
Execution
Bug Detection
Test Report

Target artifact: auditable agent traces and reports — not a black-box “fully automatic” button.

Lab constraints

Installable first

If it cannot run via Docker, CLI, or ClawHub, it is not shipped.

Review stays in the loop

LLMs draft faster; Test / Dev / Product judgment decides what merges.

Evidence over claims

Live requires a repo, image, or case study; Exploring only states method and target artifacts.