WHartTest - AI-Powered Intelligent Test Case Generation Platform

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Overview

WHartTest is an AI-powered intelligent testing platform built on Django 5.2 + DRF and modern LLM technologies. The platform adopts a frontend-backend separated Monorepo architecture, consisting of 6 sub-projects (Django backend, Vue frontend, UI automation executor, MCP tool service, Agent skill library, and online document editor). By aggregating natural language understanding, knowledge base retrieval, and embedding search capabilities, combined with LangChain/LangGraph and MCP (Model Context Protocol) tool calls, it achieves automated generation, management, and execution of test cases from requirements to executable test cases, providing a complete intelligent testing management solution for QA teams.

Core Features

1. AI-Powered Test Case Generation

• Automatically generate test cases from requirement documents, business descriptions, and knowledge base context.
• Use LangChain/LangGraph to orchestrate multi-step reasoning workflows, including context completion, test case optimization, and result tracking.
• Support Prompt templates, test case templates, and generation strategy configuration to help teams standardize testing practices.

2. Requirement Management and Intelligent Review

• Support requirement document upload, parsing, viewing, and online editing, covering the full workflow from requirement import to review reports.
• Support requirement splitting, specialized analysis, context checks, and versioned report viewing.
• Combine requirement content with knowledge bases and LLM capabilities to identify missing information, ambiguity, and key testing concerns.

3. Test Case Management

• Manage functional test cases by project and module, including case name, priority, type, preconditions, steps, expected results, and notes.
• Provide both list view and mind map view, supporting quick module/case creation, editing, copy/paste, drag-and-drop organization, import, and export.
• Support fully automated AI writing of test cases: generate complete case structures and test steps from requirement documents, business context, knowledge bases, and existing cases.
• Support fully automated AI editing, optimization, and repair of test case content, including completing preconditions, rewriting steps, improving expected results, fixing non-standard cases, and increasing coverage.
• Support fully automated AI execution of test cases with execution process recording; automatically analyze failure causes from logs, screenshots, and reports, and generate repair suggestions.
• Support test suite management, case selection, batch organization, and execution history tracking.

4. API Automation Testing

• Manage API testing assets such as API modules, interface definitions, environment variables, global headers, database configurations, functions, and tags.
• Support API test case orchestration, test task creation, task execution, result details, and test report viewing.
• Support fully automated AI writing of API test cases: automatically generate test steps, assertions, variable extraction, and pre/post scripts from API definitions, request parameters, response structures, and business context.
• Support fully automated AI editing and repair of API testing configurations, including request parameters, headers, environment variables, database checks, function scripts, assertion rules, and dependent variables.
• Support API debugging, case execution, and batch task execution; AI can automatically analyze failed results, execution logs, and response content to locate issues and generate repair plans.
• Support API synchronization, environment switching, and multi-project isolation for unified API automation management.

5. UI Automation Testing

• Built-in UI automation executor capabilities, including executor management, environment configuration, page objects, page steps, public data, element locators, and case step management.
• Support fully automated AI writing of UI automation cases and scripts: automatically generate page operation steps, element locators, assertions, and test data from pages, elements, business flows, and test objectives.
• Support fully automated AI editing and repair of UI automation steps, analyzing and generating repair suggestions for issues such as invalid locators, unstable waits, abnormal page states, and assertion failures.
• Support fully automated AI execution of UI cases, debug runs, and batch execution; collect logs, screenshots, videos, and Trace data for playback and troubleshooting.
• Integrate with test cases and the task center to provide unified scheduling and tracking for UI automation tasks.

6. Agent Chat and MCP Tool Calls

• Provide a LangGraph-based intelligent chat entry point for test analysis, test case generation, and tool calls within project context.
• Support remote MCP configuration management, enabling Agents to call external tool services and extend testing, analysis, and automation capabilities.
• Support tool approval, system prompts, token usage display, and multi-model configuration to improve controllability and security of AI calls.

7. Knowledge Base and Retrieval Augmentation

• Support knowledge base creation, document upload, document chunking, vectorization, retrieval queries, and statistics.
• Support embedding models, Reranker, global knowledge base configuration, and connection testing.
• Turn product documents, API documents, and business rules into project knowledge, providing RAG context for AI generation and review.

8. Skills Library

• Support Skills installation, management, and skill store source configuration to extend Agent capabilities.
• Support automation skills such as Playwright, enabling AI to work closer to real testing scenarios.
• Skills have high system privileges and are strongly recommended to be used only in intranet or trusted environments.

9. Task Center and Execution Tracking

• Provide a unified task center for creating, executing, and viewing automation tasks.
• Support task log viewing, UI test case selection, execution status tracking, and failure troubleshooting.
• Combine functional cases, API testing, and UI automation into a unified test execution entry point.

10. Project, Team, and System Management

• Support project management, user management, organization management, permission management, and API Key management.
• Support LLM configuration management, multi-model integration, version update prompts, dark/light themes, and Chinese/English switching.
• Support operation log recording and cleanup configuration for auditing key platform operations.

Quick Start

Docker Deployment (Recommended - out of the box)

# 1. Clone the repo
git clone https://github.com/MGdaasLab/WHartTest.git
cd WHartTest

# 2. Prepare config (use defaults with auto-generated API Key)
cp .env.example .env

# 3. One-command start (choose one of the two)
# Option A: use the deployment script (recommended, auto-selects registry mirrors)
./run_compose.sh

# Option B: use docker-compose directly
docker-compose up -d

# 4. Open the system
# http://localhost:8913 (admin/admin123456)

That's it!

Unified Deployment Script

If you use the built-in deployment script, it now asks you to choose between remote image pull and local image build at startup:

./run_compose.sh

⚠️ Production note: Log in to the admin panel, delete the default API Key, and create a new secure key.

Contact

For questions or suggestions: - Open an Issue - Use the project Discussions - When adding WeChat, please mention github!!! We will invite you to the WeChat group. - Join the group to get the latest updates and Skills.

When adding WeChat: please mention Github or WHartTest!!!

IMPORTANT SECURITY NOTICE: Skills Permissions and Deployment Safety (v1.4.0 and later)

Because the Skills module has high system execution privileges, please take the following security precautions:

Deployment recommendation: Only deploy in an intranet or trusted private network. Access control: Do not expose the service to the public Internet or grant access to unauthenticated or untrusted users. Disclaimer: This project (WHartTest) is for learning and research purposes only. Users are responsible for all security risks and consequences caused by unsafe deployment (such as public exposure or missing authentication). The WHartTest team is not liable for any security incidents, including data leaks or server compromise, caused by improper configuration.

WHartTest - AI-powered test case generation that makes testing smarter and development more efficient!