chenjigang4167/testhub_platform

TestHub一站式AI智能测试平台 · By 大刚（公众号：测试开发实战）

TestHub – AI‑powered full‑stack test management platform

What it is – TestHub is a web application that helps QA teams manage the whole testing lifecycle. It combines the usual test‑case, test‑suite and defect tracking features of a test‑management tool with a set of automation engines (Selenium, Playwright, Airtest, Locust) and, importantly, AI‑driven helpers that can generate test cases from requirement documents, drive browsers automatically, and answer testing‑related questions via a Dify chatbot.

Key capabilities

Area What TestHub provides
AI‑assisted requirement analysis Upload a PDF/Word/TXT spec; the integrated LLM (DeepSeek, OpenAI, Anthropic, Gemini, etc.) extracts business requirements and creates initial test cases.
Test‑case authoring & review Full CRUD, versioning, tagging, multi‑level review workflow with templates and checklists.
API testing HTTP & WebSocket support, hierarchical test‑suite organization, variable substitution, assertions, scheduled runs, Allure reports, email/Webhook notifications.
Web UI automation Selenium & Playwright engines, multi‑browser (Chrome/Firefox/Edge), visual script editor, POM support, AI “browser‑use” mode that lets the LLM understand page structure and generate steps (text‑ or vision‑based).
Mobile (Android) automation Airtest image‑recognition, local emulator or remote device handling, resource locking, component library, Celery‑backed async execution with real‑time WebSocket progress.
Performance testing Locust‑based load‑test jobs with metrics dashboards and reports.
Data factory >50 utilities (random data, encoding, encryption, JSON helpers, cron‑expr parser, etc.) that can be referenced directly in API or UI tests.
Collaboration & security JWT double‑token auth with refresh/blacklist, role‑based project permissions, email + enterprise‑messenger (WeChat, DingTalk, Feishu) webhooks.
Extensible AI integration Plug‑in style support for many LLM providers; model selection per role (case writing, review, browser‑use).

Architecture

  • Backend: Django 4.2 + DRF, MySQL, Redis (optional for Celery/WebSocket), JWT auth, Celery + APScheduler for async & scheduled jobs, Channels/Daphne for WebSocket, Allure for reporting, LangChain‑OpenAI & browser-use for AI calls.
  • Frontend: Vue 3 + Vite, Element Plus UI library, Pinia store, Vue‑Router, Axios, Monaco editor for script editing, ECharts for visual dashboards, vue‑i18n for multilingual UI.
  • Automation engines: Selenium, Playwright, Airtest (with OCR), Locust.
  • Deployment options: Local dev with runserver/daphne, Docker Compose (includes DB, Redis, Celery workers), optional Java runtime for Allure report generation.

Typical workflow

  1. Import requirements → AI extracts functional points → auto‑generated test cases appear in the Requirement Analysis module.
  2. Review & refine → Team members use the built‑in review process to approve or edit cases.
  3. Build test suites → Combine API, UI, or mobile tests; attach data from the Data Factory.
  4. Execute → Run synchronously (dev) or asynchronously via Celery; monitor progress via WebSocket UI; results stored and visualized with Allure and ECharts.
  5. Report & track defects → Auto‑linked defect entries, notifications to email/enterprise messengers.

Getting started (as described in the README)

  • Install Python 3.12, Node 18+, MySQL 8, optional Redis & Java 17.
  • Clone repo → create virtualenv → pip install -r requirements.txt.
  • Copy .env.example to .env and fill DB/Redis/AI‑API keys.
  • Create MySQL database testhub and run Django migrations + createsuperuser.
  • (Optional) run helper commands to load UI locator strategies and Android component packs.
  • Start backend (python manage.py runserver or Daphne for WS) and frontend (npm install && npm run dev).
  • Access UI at http://localhost:3000, API docs at http://localhost:8000/api/docs/.
  • Docker alternative: ./build-and-push.sh then docker‑compose up -d.

Who might use it

  • QA teams that want a single portal for manual case management, automated API/UI/mobile testing, and AI‑assisted test generation.
  • Organizations looking to integrate multiple LLM providers without building their own inference stack.
  • Companies needing scheduled test runs, performance testing, and rich reporting (Allure, ECharts) in a self‑hosted solution.

License – GPL 3.0 (open source, but any derivative must also be GPL‑licensed).

All details above are taken directly from the repository’s README; no additional features have been inferred.

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