OptiQA
Project: OptiQA
Tagline: Intelligent, AI-powered quality assurance that keeps pace with modern development.
Team: Quantum
Project Status: In Development
Overview
Software quality assurance today is often manual, repetitive, and slow — creating a bottleneck in the software development lifecycle. QA teams spend countless hours writing test cases, running regression tests, and documenting results, while development continues to accelerate. OptiQA is an AI-powered platform that automates critical parts of the QA process. It generates, executes, and validates test cases intelligently, ensuring higher accuracy and faster release cycles. By combining NLP, ML models, and automation pipelines, OptiQA transforms QA from a manual chore into a continuous, intelligent process. Our vision: QA that’s adaptive, fast, and reliable — keeping up with the speed of innovation.
The Problem
Manual QA is time-consuming and does not scale with modern agile development. Test coverage is often incomplete, leaving gaps that result in production bugs. QA engineers spend too much time on repetitive tasks (regression, log reviews, bug reporting). Companies face higher costs and risks due to human error and inefficient QA cycles.
Our AI Solution
OptiQA is an AI-powered Quality Assurance automation platform designed to simplify and accelerate the testing process for web applications. Its purpose is to:
- Reduce manual testing effort.
- Automatically generate, execute, and validate test cases using AI.
- Enable QA engineers and developers to test faster, smarter, and more efficiently.
- Provide detailed, real-time test results and maintain prompt/test history for traceability.
Key Capabilities
- AI-driven Test Generation: Users can input natural language prompts; AI converts them into executable test cases.
- Automated Execution: Playwright executes the generated test scripts automatically.
- Result Visualization: Users can view detailed results for each test run — including pass/fail status, screenshots, and logs.
- Prompt History Management: All user prompts and corresponding results are stored and retrievable.
- User Authentication: Secure login/signup using JWT-based authentication.
Technology Stack
| Layer | Technology |
|---|---|
| Frontend | React.js + Tailwind CSS |
| Backend | Node.js + Express.js |
| Database | PostgreSQL |
| AI/ML | OpenAI GPT-4o-mini |
| Test Execution | Playwright |
| Authentication | JWT (JSON Web Tokens) |
| Hosting (Optional) | AWS / Render / Vercel |
Workflow Description
- User Registration/Login → User signs up or logs in.
- Prompt Submission → User enters URL + natural language steps (e.g., “Login to Gmail and verify inbox count”).
- AI Processing → Backend sends the prompt to GPT-4o-mini to generate Playwright test script.
- Test Execution → The system executes the generated code using Playwright.
- Result Storage → Logs, status, and screenshots are saved in the database.
- Result Visualization → User views the detailed report and test history from the frontend.
** System Architecture**
Architecture Overview
OptiQA follows a modular microservice-inspired structure:
Frontend (React.js)
Handles user interaction, authentication, prompt submission, and data visualization.Backend (Node.js + Express)
Manages user requests, invokes GPT-4o-mini for AI processing, executes Playwright tests, and interacts with PostgreSQL.AI Layer (OpenAI GPT-4o-mini)
Generates test scripts based on natural language prompts.Database Layer (PostgreSQL)
Stores users, prompts, test results, execution logs, and screenshots.Playwright Test Engine
Executes generated test scripts and sends results back to the backend for storage and visualization.
Architecture Diagram
High Level Design (HLD)
The system follows a modular, scalable architecture that connects frontend, backend, AI, and database layers.
The frontend communicates securely with backend APIs, which orchestrate test execution, AI analysis, and reporting.
Key Components
- Frontend: Interactive dashboard for test management, execution tracking, and result visualization.
- Backend: API layer managing test execution requests, log collection, and AI interactions.
- Database: Centralized storage for test cases, execution results, and logs.
- AI/ML Layer: Processes logs, predicts failure patterns, and generates intelligent test suggestions.
- Authentication: Uses JWT tokens for secure and stateless user sessions.
HLD Diagram
LLD Diagram
Core Modules
- Test Case Manager: Converts unstructured requirements into structured, testable cases using NLP.
- Execution Engine: Runs automated Playwright tests and captures execution logs.
- AI Analyzer: Combines rule-based logic and NLP for anomaly detection and failure summaries.
- Log Parser: Normalizes error logs from different environments for consistent analysis.
- Report Generator: Aggregates and visualizes results into actionable insights.
Database Design
Tables
users
| Column | Type | Description |
|---|---|---|
| id | UUID (PK) | Unique user identifier |
| name | VARCHAR | User’s full name |
| VARCHAR | Unique email address | |
| password_hash | TEXT | Encrypted password |
| created_at | TIMESTAMP | Timestamp of registration |
prompts
| Column | Type | Description |
|---|---|---|
| id | UUID (PK) | Unique prompt identifier |
| user_id | UUID (FK → users.id) | Owner of the prompt |
| prompt_text | TEXT | User-entered prompt |
| generated_code | TEXT | AI-generated Playwright test code |
| created_at | TIMESTAMP | Date/time of prompt creation |
test_results
| Column | Type | Description |
|---|---|---|
| id | UUID (PK) | Unique test result identifier |
| prompt_id | UUID (FK → prompts.id) | Related prompt |
| status | VARCHAR | Test status (e.g., PASSED, FAILED, ERROR) |
| execution_log | TEXT | Execution logs |
| screenshot_url | TEXT | Optional screenshot of result |
| created_at | TIMESTAMP | Timestamp of execution |
Challenges & Learnings
Converting unstructured requirements into testable cases. Handling variability in logs and error patterns. Ensuring scalability across different projects and teams. Combining NLP + rule-based logic works better than pure ML in early stages. QA automation isn’t just about test execution — it’s about actionable insights. Building a polyglot system (Flutter + Node.js + Python) requires tight orchestration.
Future Roadmap
Short-term goal - Implement regression automation and intelligent log parsing. Medium-term goal - Fine-tune NLP models to generate end-to-end test suites. Long-term goal - Adaptive QA assistant that self-learns from previous test cycles and suggests new edge cases automatically.
Repository & Live Demo
GitHub Repository: FE - https://gitlab.enovate-it.com/enovate/hackathon/optiqa-fe BE - https://gitlab.enovate-it.com/enovate/hackathon/optiqa-be
Categories: #project #ai-hackathon-2025 #category-QA #category-automation #category-ai