Aerospace software teams face a challenge that extends beyond executing tests: demonstrating that the right requirements have been verified, changes have been evaluated, and the resulting evidence reflects the correct software configuration. When requirements, test cases, and results sit in disconnected systems, establishing these connections becomes time-consuming.
AI-driven requirements traceability can help accelerate testing and certification preparation by improving requirements quality, highlighting missing relationships, and supporting analysis across connected engineering information. Combined with requirements-based testing and controlled evidence management, this approach helps teams identify gaps earlier and reduce avoidable rework.
For aerospace projects working toward DO-178C objectives, the opportunity is practical: make the path from requirement to verification evidence easier to manage, inspect, and maintain throughout development.
What Is AI-Driven Requirements Traceability?
AI-driven requirements traceability uses artificial intelligence to assist with analyzing relationships between requirements and associated engineering artifacts, such as designs, test cases, test results, and change records.
Conventional traceability records these relationships. AI adds assistance with interpreting the information behind them, for example, flagging a potentially ambiguous requirement, suggesting a relevant test relationship, or summarizing the possible impact of a change.
Visure’s Engineering Intelligence approach connects AI agents with requirements, traceability, risks, and verification information through the VISURE MCP Server. This provides engineering context for AI-assisted workflows while retaining governance and lifecycle accountability.
The value depends on the quality of that context. An AI suggestion is useful only when engineers can inspect its source, assess its reasoning, and confirm whether the proposed relationship is correct.
Why Traceability Matters for DO-178C Certification
DO-178C addresses software considerations in airborne systems and equipment certification. The FAA recognizes it through Advisory Circular AC 20-115D as an acceptable means of demonstrating compliance for the software aspects of airborne systems. Its scope includes planning, development, verification, configuration management, quality assurance, and certification liaison.
Within this lifecycle, traceability helps teams investigate questions that matter during verification and review:
- Which requirements have associated test cases and reviewed results?
- Which tests need reassessment after a requirement changes?
- Where are missing links, unresolved discrepancies, or outdated evidence?
- Does the evidence belong to the software configuration under review?
A populated traceability matrix alone cannot answer all these questions. A requirement may be linked to a test that no longer reflects its current behavior. A passing result may belong to an earlier build. Effective requirements traceability therefore requires both maintained relationships and engineering review of what those relationships demonstrate.
Improve Requirements Quality Before Testing Begins
Efficient requirements-based testing starts with requirements that describe observable, verifiable behavior. Ambiguous wording forces test engineers to interpret intent, increasing the likelihood of inconsistent test cases and late clarification.
Consider an illustrative requirement: “The system shall display the warning quickly.”
Before designing a meaningful test, engineers need an agreed triggering condition, a measurable response time, and any relevant operating conditions. AI-assisted requirements analysis can help flag this ambiguity for review. Engineers must then establish and approve the intended behavior.
Visure describes Vivia as providing governed AI support across requirements, testing, risk, and traceability, with human approval in the workflow. Applied early, this assistance can help teams prepare clearer inputs for verification.
The practical goal is to resolve uncertainty while requirements are being developed, before it spreads into implementation and testing.
Connect Requirements to Test Implementation and Execution
Requirements management and test execution become more useful when information moves between them with its identity and context preserved.
RapiTest supports requirements-based testing and interfaces with requirements management tools. Rapita documents a workflow involving requirements import, assignment to tests, reporting, and export, including importing Visure requirements through ReqIF. RapiTest also supports on-target execution and presents expected and actual results when checks fail.
Visure and RapiTest’s previously published joint webinar describes executing safety-critical test cases in RapiTest, importing results, and generating requirements-to-test traceability reports. This provides a practical foundation for connecting requirements management with verification activities.
A useful workflow follows five stages:
| Stage | Engineering activity | Intended outcome |
| Define | Review and baseline verifiable requirements in Visure. | Establish an agreed basis for testing. |
| Connect | Transfer requirements and associate them with relevant tests. | Preserve the relationship between intent and verification. |
| Execute | Implement and run requirements-based tests in RapiTest. | Capture observed behavior and test outcomes. |
| Review | Bring results and reports into the requirements workflow. | Investigate failures, missing evidence, and discrepancies. |
| Maintain | Reassess affected relationships when requirements or software change. | Keep verification evidence aligned with the project baseline. |
Teams should agree how identifiers, versions, result status, and configuration information are handled before relying on the exchanged data.
Combine Requirements Coverage with Structural Coverage Analysis
Requirements coverage and structural coverage answer different questions.
Requirements coverage examines whether verification activities adequately address the specified behavior. Structural coverage examines which parts of the implemented code were exercised during testing. A complete set of requirement links does not, by itself, demonstrate sufficient structural coverage.
Rapita’s RapiCover supports structural coverage analysis, including MC/DC, and produces evidence for safety-critical software verification. It complements requirements-based testing by helping engineers examine code execution during tests.
Together, these perspectives support more focused investigation. An uncovered code segment may indicate a missing test, an incomplete requirement, or another issue requiring engineering analysis. The appropriate response follows from understanding the cause, rather than simply adding tests to increase a coverage figure.
Use Engineering Intelligence to Manage Change
Requirements changes can affect test procedures, expected results, implementation, and existing evidence. The challenge is identifying those consequences before outdated information is reused.
With connected lifecycle data, AI-assisted analysis can help summarize the relationships surrounding a changed requirement and highlight items for review. Visure’s published approach to AI-enabled change management emphasizes structured ALM information, traceability, and human accountability for decisions.
For example, if an approved warning response time changes, a proposed review workflow could identify linked tests, flag their expected results for reassessment, and highlight previous execution records that may no longer establish the revised behavior.
This makes impact analysis more actionable. Engineers retain responsibility for confirming the affected scope, deciding which verification activities must be repeated, and approving the updated evidence.
Organize Certification Evidence Throughout the Lifecycle
Certification preparation becomes harder when teams postpone evidence organization until the final review. A more effective approach is to maintain the relationship between approved requirements, verification activities, results, and unresolved findings as work progresses.
For each relevant verification result, teams should be able to establish what was tested, which procedure was used, what configuration was involved, and how discrepancies were handled. AI-assisted summaries can help reviewers navigate this information, provided the underlying records remain accessible.
The use of automation also needs to fit the project’s assurance approach. FAA guidance recognizes DO-330 for software tool qualification; teams should assess qualification needs according to the tool’s intended use and the reliance placed on its output. AI-generated recommendations do not independently establish compliance or replace required verification activities.
See the Requirements-to-Testing Workflow in Action
Join Antoine Colin, CTO at Rapita Systems, and Fernando Valera, CTO at Visure Solutions, on December 1st at 11:00 AM EST for “Accelerating Testing and DO-178C Certification with an AI-Driven Requirements Traceability Approach.”
Through a live aerospace project example, the session will connect requirements management, safety-critical testing, and certification evidence. Explore how AI and Engineering Intelligence can support requirements quality, how RapiTest enables test implementation and execution, and how traceability analysis helps teams investigate verification gaps.
Check out the joint Visure and Rapita webinar to discover practical ways to streamline aerospace testing and strengthen DO-178C certification readiness.
