AI-Generated Code for Critical Systems: Can We Trust It?

Generative AI is transforming embedded software development. Developers can now generate code, create unit tests, modernize legacy applications, and even automate defect remediation in seconds. The productivity gains are undeniable, but for safety-critical systems, one question matters above all else:

Can AI-generated code be trusted?

In this webinar, Parasoft’s Ricardo Camacho and Miroslaw Zielinski explore where AI delivers real value, where the risks remain, and how organizations can safely integrate AI into embedded software development without compromising quality, compliance, or engineering control.

You’ll discover why the future isn’t about replacing engineers with AI, it’s about combining AI-assisted development with deterministic verification, continuous testing, and human oversight to build software that can be trusted.

Watch the webinar to learn:

• Why AI-generated code requires the same rigorous verification as human-written software
• The strengths and limitations of generative AI in safety-critical and embedded development
• How static analysis, unit testing, structural code coverage, and coding standards verification validate AI-generated code
• How deterministic feedback loops enable AI to iteratively improve code quality
  Best practices for integrating AI into CI/CD pipelines while maintaining traceability, accountability, and compliance
• Where AI-assisted remediation fits into modern software verification workflows
  Emerging trends shaping the future of AI-assisted software engineering
• Best practices for integrating AI-assisted development into CI/CD pipelines while maintaining traceability, accountability, and human oversight
• Emerging industry trends shaping the future of AI-assisted software engineering

Whether you’re evaluating AI adoption or already incorporating AI into your development workflow, you’ll leave with practical guidance for balancing productivity gains with the verification rigor required to build software that is safe, secure, reliable, and trustworthy.

The shift in embedded development

AI coding assistants are moving from simple code completion to more advanced tasks like refactoring, documentation, and even autonomous remediation of defects. In general-purpose software, a crash might just be an annoyance. In the world of embedded systems, however, code failure can result in physical or environmental harm.

Because embedded software is often governed by strict industry standards—like ISO 26262 for automotive or IEC 62304 for medical devices—the process of development must be transparent and verifiable. The industry is currently balancing the convenience of AI tools with the traditional, deterministic engineering practices that ensure reliability.

Understanding the risks of generative AI

Unlike traditional compilers or well-defined tools, AI models are probabilistic. They function on patterns learned from data, which means they do not guarantee the same outcome every time. Research has shown that even sophisticated AI models can introduce security vulnerabilities and coding standard violations that are not immediately obvious to the developer.

Moving toward a hybrid engineering workflow

Rather than viewing AI as a replacement for engineering, many experts suggest a hybrid approach. This method involves keeping the human-in-the-loop and using automated quality gates to act as a “check” on AI performance. By integrating static analysis, unit testing, and structural code coverage into the CI/CD pipeline, teams can create a loop that catches errors automatically.

1. AI generates the initial implementation of the code.
2. Deterministic tools run checks for security and compliance violations.
3. If issues arise, the tool provides specific diagnostics to the AI.
4. The AI uses that feedback to refine the code.
5. The cycle continues until the code meets the required quality gate.

Bridging productivity and compliance

This iterative process doesn’t replace the human engineer; it changes what the engineer works on. Instead of spending hours fixing repetitive compliance errors, developers can focus on high-level architecture and solving complex problems. When AI is restricted to a structured environment with clear engineering feedback loops, it stops being a liability and starts becoming a powerful, reliable assistant for the modern embedded development team.

Live Demonstration Included

See these concepts applied in a real development workflow.

During the live demonstration, you’ll watch AI work alongside deterministic verification tools to identify software defects, remediate coding standards violations, improve code quality, and support continuous verification—all while keeping engineers firmly in control of every change.

Rather than treating AI as a replacement for engineering judgment, you’ll see how AI can become a powerful productivity tool when guided by deterministic analysis, automated quality gates, and human review.

Whether you’re evaluating AI for the first time or already incorporating AI into your development process, this webinar provides practical guidance for adopting AI responsibly while continuing to build software that is safe, secure, reliable, and compliance-ready.