Discover TÜV-certified GoogleTest with Agentic AI for C/C++ testing!
Get the Details »
WEBINAR
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:
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.
AI coding assistants are rapidly evolving beyond code completion. Developers can now use AI to generate code and tests, refactor and modernize applications, and even identify and automatically remediate defects within CI/CD workflows.
The productivity potential is enormous. But safety-critical software operates under fundamentally different constraints, 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.
A defect in a consumer application may be an inconvenience. A defect in a critical embedded system can lead to security vulnerabilities, equipment or mission failure, environmental damage, injury, or loss of life.
So the real question is not how quickly AI can generate software.
Can that software be verified to the level of confidence required for a high-assurance system?
AI-generated code can be remarkably convincing. It may compile, execute correctly, and appear well structured during code review.
But appearance is not evidence.
Because AI models are probabilistic, they cannot guarantee functional correctness, security, coding standards compliance, or consistent results. Generated code may still contain subtle logic errors, vulnerabilities, unsafe constructs, and other defects that are difficult to detect.
Even when explicitly instructed to follow specific coding standards or engineering requirements, AI cannot guarantee compliance.
So, the question is not whether AI is getting better at generating software. It clearly is. The question is whether the AI itself can provide evidence that the resulting software is correct.
For safety-critical development, the answer is no.
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.
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.
AI will continue to transform software engineering.
The question is no longer whether development teams will use AI. The more important question is how organizations will integrate AI into engineering processes without compromising software quality, security, safety, or compliance.
For safety-critical software teams, the answer will likely be found in the combination of AI assistance, deterministic verification, continuous testing, engineering traceability, and human oversight.
AI can generate code.
AI can generate tests.
AI can recommend and implement changes.
AI can help engineers work faster.
But AI cannot provide the objective evidence required to demonstrate that critical software is safe, secure, reliable, and compliant.
That evidence must come from rigorous engineering and verification.
The future of safety-critical software development is not about trusting AI. It is about building engineering workflows that allow us to use AI without having to trust it blindly.
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.
