🌐 Codestral ROS2 Gen: Network Scanner Extension Now Available!

⚡️Here it is - the second release of Codestral ROS2 Gen with a powerful new feature: the NetworkScanner!

🔍 What's New in This Release
The NetworkScanner module with efficient network discovery capabilities:
• Fast ICMP-based scanning for host discovery
• Asynchronous packet handling for optimized performance
• Configurable scan parameters (timeout, interval, etc.)
• Seamless integration with the existing ROS2 generation pipeline

🛠 How It Works
The NetworkScanner uses optimized ICMP echo requests (pings) to detect active hosts on a network. It employs an innovative approach with:
• Synchronous packet sending for precise timing control
• Asynchronous response collection for efficient handling
• Smart timeout management for reliable results
• Clean ROS2 message publishing for network status information

🧪 Key Components
• network_scanner.py: Core scanning orchestration
• network_host.py: Host state management
• scan_operation.py: Context-managed scanning operations
• network_parser.py: Network targets parsing

Full codebase documentation is available on projects's GitHub pages 📙

🚀 Try It Yourself
The detailed example documentation shows how to generate your own network scanner nodes. You can even use the standalone nscan command-line tool for quick testing!

This extension builds upon core generation system, demonstrating how the Codestral generator can create complex, functional ROS2 components with system-level interactions. 🤖

#ROS2 #AI #NetworkScanning #Robotics #CodeGenerating #Codestral

👨‍🔬Testing Results: ROS2 Network Scanner Generation

I want to share the results from my test of ROS2 Network Scanner generation example.

After running 30 iterations of generating the ROS2 Network Scanner:
• Total test duration: ~6 hours 15 minutes
• Average successful generation time: ~2 minutes per attempt
• Distribution of attempts: Right-skewed (median: 4, mean: 6.7)

This means that, on average, the generator produces working code in about 13 minutes - quite reasonable performance for automated code generation in my opinion!

Failure Analysis
Looking at where generation stopped, the distribution clearly demonstrates the generator's stability:
• Over 80% stopped at the testing stage
• ~15% were successful attempts
• Only about 5% failed during the PARSING or GENERATION stages

Test Coverage Patterns
Examining the test pass rates revealed two distinct patterns:
• Basic functionality (7 tests): Node startup with valid/invalid parameters and handling overlapping scans using nscan utility
• Advanced scenarios (9 tests): Including handling invalid JSON format in the node <-> nscan interface and managing outdated scan results

This suggests that generating code with specific behavior for edge cases remains challenging.

I've included all metrics and analysis notebooks in my project repository, so feel free to explore the data yourself!

#ROS2 #AI #NetworkScanning #Robotics #CodeGenerating #Codestral #testing