Service Overview
Unit Test AI
Automated test case generation service using AI to create comprehensive unit tests from models and requirements. Reduces generation time from 20–40 minutes to just 5–7 minutes per task at 85–90% accuracy. Applied to Model-Based Design processes, achieves 70% automated rule-check coverage and 35% reduction in manual testing effort — cutting development cycles by 30–40%.
85–90%
accuracy
5–7 min
per task
35%
effort reduction
70%
rule coverage
30–40%
dev cycle reduction
Capabilities
Key capabilities
Automated Test Generation
AI generates comprehensive unit tests from source code, reducing manual test writing effort significantly.
Code Coverage Optimization
Intelligent test generation targeting uncovered code paths for maximum coverage.
Digital Cockpit Expertise
Specialized models trained on automotive cockpit software patterns and requirements.
IVI/Cluster/HUD Support
Comprehensive testing for all major cockpit components.
CI/CD Integration
Seamless integration with continuous integration and deployment pipelines.
Technology
Technology stack
| Component | Technology | Purpose |
|---|---|---|
| AI Model | LLM, GPT-based | Test generation |
| Testing Framework | JUnit, CppUnit, GoogleTest | Test execution |
| Code Analysis | Static analysis tools | Code understanding |
| CI/CD | Jenkins, GitLab CI | Automation pipeline |
| Development | Python, C++, Java | Implementation |
Use cases
Real-world applications
Documented outcomes from actual deployments.
MBD Check-Rule Automation
AI Check-Rule Model automates rule violation detection in Model-Based Design, achieving 70% coverage of rule checks and saving approximately 20 minutes per model.
Before
30–40 minutes manual rule checking per model, consistency issues, human fatigue errors
After
AI achieves 70% check coverage automatically; 20 minutes saved per model
AI Model Performance Validation
Comprehensive validation pipeline for automotive AI models — ensuring performance targets are met across accuracy, recall, and inference speed before production deployment.
Before
Inadequate baseline performance for real-time safety-critical requirements
After
5.99% accuracy gain, 49.15% recall improvement, 18.7× faster inference (quantized)
How we work
Implementation approach
Phase 1: Codebase Analysis
- Analyze existing codebase and test patterns
- Identify key components and testing requirements
- Define test generation rules and constraints
Phase 2: Model Training & Customization
- Train AI models on automotive cockpit code patterns
- Customize for specific OEM requirements
- Validate test quality and coverage
Phase 3: Integration & Validation
- Integrate with CI/CD pipeline
- Validate generated tests against existing test suites
- Ensure compatibility with testing frameworks
Phase 4: Deployment & Optimization
- Deploy to development teams
- Monitor test generation quality and coverage
- Continuously improve based on feedback
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