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AI is revolutionizing Application Lifecycle Management (ALM) by bridging traceability gaps through intelligent automation, real-time insights, and seamless linking of requirements, code, and tests.
How AI is Closing the Traceability Gap in ALM

From Code to Compliance: How AI is Closing the Traceability Gap in ALM

Guest Article for Nanga Blog via our Partner Raiqon

As products grow more complex and deeply integrated into our lives, regulatory requirements are tightening to ensure user safety. For anything beyond the simplest systems, professional Application Lifecycle Management (ALM) platforms are essential. Tools like Codebeamer have become indispensable: They orchestrate engineering workflows, enable end-to-end traceability, and simplify compliance.

Traceability spans the entire lifecycle, from user needs to requirements, tests, and risks. Ideally, it should extend all the way to the code level. But while most teams understand the value of connecting requirements to the code that fulfills them, actually establishing that link remains a real challenge, especially in agile environments.

The advent of AI has the potential to be a true game changer in this space. Raiqon, a Nanga Systems partner, has developed an AI-driven platform for ALM that integrates seamlessly with Codebeamer. Among its key capabilities is the automatic generation of requirements directly from source code.

The Role of Code and Requirements in ALM

ALM platforms like Codebeamer are built to manage structured, text-based artifacts like requirements, risks, test cases, and more. Code, however, lives in separate repositories like Git or Bitbucket. While linking code to requirements is technically possible, it’s far from seamless. A key challenge lies in defining what exactly a requirement should trace to: a file? A specific commit? A function or even a single line of code?

Ideally, every requirement should link directly to the relevant parts of the code—and vice versa. This kind of traceability enhances collaboration, supports impact analysis, improves test coverage, and simplifies compliance. But in reality, achieving this level of integration is anything but straightforward.

Traceability: A Compliance Necessity or a Best Practice?

In regulated industries like aerospace or automotive, traceability between requirements and code isn’t optional; it’s a mandate. Standards like ISO 26262 or DO-178C demand rigorous documentation, including mid-level requirements linked explicitly to the code implementing them.

But even in less strictly regulated sectors, traceability is strongly recommended due to its many advantages. It promotes transparency, helps identify inconsistencies early, and builds confidence when managing changes.

Too often, traceability is seen as a tedious and manual burden imposed by regulators. But when done right, it delivers genuine value. Here’s what organizations stand to gain:

  • Improved Change Impact Analysis: Know exactly which code is affected by a requirements change.
  • Faster Onboarding: New team members understand how the system works and why.
  • Streamlined Audits: No more last-minute scrambles to produce documentation.
  • Higher Code Quality: Gaps in coverage and dead code become obvious.

Agile Makes Traceability Even Harder

The traditional waterfall model already struggles to keep code and requirements aligned. Modern best practices recommend shifting to agile development, which offers clear benefits such as shorter iterations and faster feedback. 

However, agility often makes maintaining traceability more difficult. Mid-level requirements are frequently documented late or not at all, turning audit preparation into a stressful and time-consuming task. This is precisely where tools like Raiqon can make a critical difference.

From Code to Requirements: How Raiqon Bridges the Mid-Level Gap

In many regulated industries, particularly automotive, compliance hinges not just on having high-level requirements, but on maintaining clear, auditable mid-level requirements that map directly to the underlying code. Yet in agile environments, this documentation often lags behind, only addressed when audits loom. This “mid-level gap” can create massive last-minute workloads and opens teams to compliance risk.

The AI-powered Raiqon platform addresses this challenge and automates the generation of mid-level requirements directly from source code. It operates securely on-premises, works across all major programming languages, and is capable of parsing entire repositories or targeted modules to produce concise, compliant, and audit-ready requirements.

"Before using AI to generate mid-level requirements, we spent endless hours drafting them just in time for audits. Now, we produce them quickly and accurately, allowing us to uphold functional safety and focus on the real engineering challenges."
Automotive Tier 1 SupplierProject Manager

What sets Raiqon apart is its proprietary AI foundation model, which is trained on domain-specific data and capable of interpreting code with the nuance of an experienced requirements engineer. This AI doesn’t just extract technical functions; it generates well-structured functional and non-functional requirements aligned with industry standards like ISO 26262, Automotive SPICE, and ISO/SAE 21434. The output can be customized to an organization’s guidelines and seamlessly formatted for integration with ALM platforms like Codebeamer.

From Weeks to Hours

One notable case study saw an automotive supplier generate 800 mid-level requirements from approximately 500,000 lines of code in under three hours, a task that would have otherwise required weeks of manual effort or external consulting. Beyond just saving time, the use of Raiqon surfaced unused code. Seeing the value, the supplier decided to start using the technology proactively, to embed traceability earlier in the development cycle rather than retrofitting it under pressure.

As Raiqon continues to evolve, future capabilities include deeper integration with version control systems, incremental analysis, and traceability management. Though initially built for automotive, its approach is broadly applicable to aerospace, medical devices, and any domain where rigorous compliance is essential.

Outlook

Traceability between requirements and code has long been costly, manual, and error-prone. AI is beginning to change that. By automating the creation of mid-level requirements, it’s making end-to-end traceability more achievable, especially for Codebeamer users seeking better integration, audit readiness, and agility.

For a deeper dive, download the whitepaper to see how AI-driven requirements can streamline compliance and development. Then connect with Nanga Systems to explore how Raiqon can be tailored to your workflow—from pilot to full-scale integration.

Guest Author

Dr. Michael Jastram is a systems engineering and requirements management expert. At Raiqon, he leads Client Solutions, helping customers boost development efficiency through AI. He also authors the blog se-trends.de/en, where he shares insights on emerging trends, and is a sought-after international speaker and consultant.