Aerospace Innovator Enhances Code Traceability with AI
AI-assisted code reviews, change tracking, and documentation accelerated development cycles for regulated aerospace systems.
Overview
A leading aerospace R&D and manufacturing company needed to improve traceability in its development process. Code changes were tracked inconsistently, reviews were manually intensive, and documentation lagged behind. In an industry where compliance and precision are non-negotiable, this presented risk and slowed time to market.
The Challenge
Code review processes were entirely manual and time-consuming
Developers lacked a consistent way to tag changes and map them to requirements
Compliance teams struggled to track review history or justify decisions
Engineering knowledge was siloed and not easily reusable across projects
The Solution
AI-Powered Code Review
Integrated AI agents into the code review workflow to perform first-pass reviews of embedded C code, identifying potential issues and suggesting improvements.
Intelligent Change Tracking
Implemented automated tagging of changes by module, type, and functional area, creating a comprehensive traceability matrix across the codebase.
Requirements Mapping
Connected code changes to tickets, requirements documentation, and test plans, establishing clear links between business needs and implementation.
Human-in-the-Loop Validation
Enabled senior engineers to review AI suggestions, override when necessary, and provide feedback to improve the system's understanding of aerospace-specific requirements.
AI-Driven Code Traceability System
Automated code review and analysis
Intelligent change tracking and tagging
Requirements-to-code mapping
Compliance documentation generation
The Impact
Reduced time spent on initial code reviews by over 50%
Improved traceability across code, tickets, and test plans
Increased confidence in audit trails for compliance teams
Helped onboard new developers faster with documented AI reasoning
Accelerated delivery cycles without compromising quality
Why It Worked
This wasn't just code linting or rule-checking. The RAP Platform enabled AI agents to embed themselves in real developer workflows — understanding business context, surfacing relevant trace links, and keeping a clean record of decisions without disrupting how teams work.
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