QA Flow is an AI-powered Quality Assurance platform designed for test developers. It enables:
- 📊 Comprehensive Test Reports – Generate, analyze, and visualize test results with detailed insights.
- 🤖 AI-Powered Issue Resolution – Get intelligent suggestions for fixing detected issues.
- 🌐 Cross-Platform Accessibility – View reports on both web and mobile applications.
- 📚 Customizable SDK & Library – Integrate our module into your test framework and personalize reports effortlessly.
✅ Automated test result analysis
✅ AI-based error resolution recommendations
✅ Web & Mobile report visualization
✅ Customizable reporting module for developers
✅ Intuitive dashboard with detailed logs
This Next.js project uses FumaDocs for documentation management. The documentation is accessible at the /docs URL path, and all documentation content is stored in the content/docs directory of the project.
The primary goal of QA Flow is to streamline the quality assurance process by providing a comprehensive platform for test reporting and analysis. With QA Flow:
- Test developers can integrate our reporting module into their test frameworks
- Test results and reports are automatically collected and processed
- Teams can view detailed test reports through an intuitive dashboard interface
- AI-powered analysis helps identify patterns and suggest fixes for recurring issues
- QA teams can track test coverage and quality metrics over time
This approach ensures faster issue detection, more efficient debugging, and ultimately higher quality software releases.
To get started with QA Flow in your Next.js project, follow these steps:
Ensure you have the following installed:
- Node.js (>= 18.x)
- pnpm
# Clone the repository
git clone https://github.com/QA-Flow/qaflow-web.git
cd qaflow-web
# Install dependencies
pnpm installpnpm devThe application will be available at http://localhost:3000.
pnpm buildTo start the production server:
pnpm startWe welcome contributions! Feel free to submit issues, feature requests, or pull requests.
This project is licensed under the MIT License.
- GitHub: @QA-Flow
- Author Github: @dorukozgen
- LinkedIn: Doruk