AI-assisted, deterministic E2E regression testing framework built on Playwright.
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Updated
Jul 15, 2026 - JavaScript
AI-assisted, deterministic E2E regression testing framework built on Playwright.
QA automation portfolio: Playwright API & UI testing, AI agent-driven test generation (planner, generator, healer), k6 & JMeter performance testing - built with an AI-native workflow
End-to-end software test suite for an Open-Lab Reservation System — combining manual/exploratory testing with AI-assisted automation: 62 JUnit5/Mockito/MockMvc tests (CI on JDK 8/21 + JaCoCo), JMeter/AB performance scripts, Playwright E2E, manual test cases & a reusable AI prompt library.
Entertainment-domain QA Automation + Media Engineering Accelerator featuring FFmpeg/HLS streaming pipelines, Cypress & Playwright automation, Python validation, k6 performance testing, AI-assisted QA workflows, and GitHub Actions CI/CD quality gates for streaming-device and media-platform testing.
Esercizio L15 - AI Coding Upskilling: test E2E con Playwright che verifica l’etichetta “intervento rapido” per un ticket con priorità alta e canale telefono.
Esercizio L14 - AI Coding Upskilling: implementazione assistita dall’AI di unit test con node:test per verificare il mapping normale + email → standard di urgencyLabel.
个人软件测试实践:使用 pytest、API 合同测试、MySQL 只读校验和证据链定位 AntFlow DIY 流程一致性缺陷;当前 PARTIAL / PASS_WITH_WARNINGS,BUG-001 未修复。
Local-first Playwright test drafting for SDET/QA workflows
Local QA memory for recurring failed-test investigations, reusable evidence, and reviewable AI-assisted QA workflows.
Laboratório acadêmico de QA no Hub de Leitura, com automação E2E em Cypress, validação via Playwright MCP, QA Agent, prompts, documentação e evidências.
Playwright + TypeScript test automation framework targeting Sauce Demo, Swagger Petstore (UI + API), and a self-hosted Express PetHub Local app. Page Object Model, typed DTOs, builder-based test data, AI-assisted workflows, and detailed bug catalogues for the public demo targets.
A practical demo repository showing how QA teams can use VS Code, AI assistants, and reusable tools to turn requirements into structured test scenarios and executable workflows. It demonstrates an AI-assisted testing process where deterministic tools handle execution while the model focuses on analysis, planning, and documentation.
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