Embedding-Driven
Requirements Management

Executive Summary

Requirements management is simultaneously the most foundational and most frequently failing discipline in complex systems engineering. In defense programs, space systems, financial platforms, and enterprise software — the environments where requirements failure costs are measured in billions, mission readiness, and lives — traditional keyword-based traceability approaches are producing false confidence. A link exists between a requirement and a test case; the link says nothing about whether the test case semantically covers the requirement's intent.

Semantic embedding technology changes this. By representing requirements, design artifacts, test cases, and system behaviors as high-dimensional vectors in a shared semantic space, organizations can measure semantic coverage, detect implicit conflicts, perform automated impact analysis, and enforce system alignment in ways that keyword matching and manual traceability matrices cannot approach. This paper presents Continuum Resources' embedding-driven requirements management framework — the research and methodology that underpins our requirements engineering practice for MBSE programs and complex enterprise systems.

Introduction: Requirements at Scale

The James Webb Space Telescope had approximately 344 potential single-point failures that required individual design solutions. The F-35 program manages over 250,000 system requirements across nine aircraft variants and multiple contractor organizations. A modern enterprise financial platform may have 50,000 functional requirements spanning regulatory compliance, business logic, security, and integration interfaces. At this scale, requirements management is not a process — it is a data management and analytics challenge for which most organizations are using tools designed for hundreds of requirements, not hundreds of thousands.

The failure mode is insidious. Requirements exist. They are documented. They have identifiers. They are linked — nominally — to design artifacts and test cases through traceability matrices maintained with varying degrees of fidelity. A program manager looking at a traceability coverage report sees green across the board. What the report does not show: that a substantial fraction of those links are semantic mismatches — a keyword in the requirement title happened to match a keyword in the test case name, but the test case tests a different behavior than the requirement specifies. The requirement is "covered" in the database. It is not covered in reality.

The Embedding Opportunity

Large language models and the transformer architectures behind them have produced a capability with profound implications for requirements management: the ability to encode natural language text as dense semantic vectors that capture meaning, not just keywords. Two sentences that express the same idea in completely different words will have high cosine similarity in embedding space. Two sentences that share keywords but describe different behaviors will have low similarity. This is the property that makes embedding-driven requirements management qualitatively different from all prior automated approaches.

The application of this capability to requirements engineering is the subject of this paper — drawing on Continuum's published research, operational MBSE practice, and the results of applying these techniques on active DoD and enterprise programs. This is not a laboratory result; it is an operational methodology.

The Requirements Problem

Requirements management failures in complex systems manifest in four primary patterns, each with compounding effects as system complexity grows:

The Cost of Requirements Failures in Defense Programs

The Government Accountability Office has documented requirements instability and traceability failures as contributing factors in the majority of major defense acquisition programs experiencing cost overruns and schedule delays. The Systems Engineering Research Center (SERC) estimates that requirements defects discovered in system testing cost 10–100× more to fix than the same defects caught at requirements review. For programs with 250,000 requirements, even a 0.1% semantic mismatch rate in traceability represents 250 potentially undetected coverage gaps — each of which is a defect incubating toward operational failure.

Traditional Approaches & Their Limits

Requirements management tooling has evolved significantly since the first requirements databases, but the fundamental analytical approach has not. Understanding why existing approaches fall short is prerequisite to appreciating what embedding-driven methods add.

Semantic Embeddings: A Technical Primer

A semantic embedding is a dense, high-dimensional vector representation of text — produced by a neural language model trained to encode the meaning of language rather than just its tokens. Requirements engineers do not need to understand the architecture of transformer models, but do need to understand three key properties that make embeddings useful for requirements analysis.

Property 1: Semantic Proximity

Requirements that express similar intent — even in completely different language — will have embeddings that are close together in the high-dimensional vector space, as measured by cosine similarity. Consider:

• REQ-001: "The system shall authenticate users before granting access to classified data."
• REQ-247: "Prior to displaying restricted information, the platform must verify user identity."

These requirements share no significant keywords — "authenticate" vs. "verify identity," "granting access" vs. "displaying," "classified" vs. "restricted" — yet an embedding model trained on natural language will place them close together because they express the same semantic intent. A keyword-based tool sees no relationship; an embedding-based tool measures a similarity score above 0.85, correctly suggesting a potential duplicate or dependency.

Property 2: Semantic Distance for Conflict Detection

Requirements that appear to address the same domain but specify conflicting behaviors will have moderate cosine similarity — close enough to indicate they concern the same system aspect, but distinct enough to flag for human review. This "conflict zone" in similarity space (typically 0.6–0.8 cosine similarity for requirements in the same domain) is invisible to keyword matching but detectable through embedding analysis.

Property 3: Cross-Artifact Semantic Coverage

The most powerful application: embeddings allow semantic comparison across artifact types. A requirement, a design element, a test case, and a code module can all be embedded in the same vector space. Cosine similarity between a requirement vector and a test case vector directly measures whether the test case semantically covers the requirement — not just whether it was manually linked to it. This is the basis for automated traceability gap detection.

Cosine Similarity Threshold Interpretation

Embedding-Driven Traceability

Traditional traceability creates a graph of explicit links between requirements and artifacts. Embedding-driven traceability creates a semantic space in which the proximity of any two artifacts reflects their meaning similarity — and derives traceability insights from that space rather than from manually curated links. The two approaches are complementary: embeddings augment and validate explicit links, and surface gaps and conflicts that manual link management cannot detect.

Automated Link Generation and Validation

The embedding pipeline generates traceability candidates by computing cosine similarities between all requirement-artifact pairs above a configurable threshold. For programs with tens of thousands of requirements and artifacts, this generates a ranked list of candidate links that focuses human review effort on the cases most likely to represent real semantic relationships — or the cases where existing links are potentially incorrect.

Link validation is the more valuable function: for every existing human-maintained traceability link, the system computes the cosine similarity between the linked artifacts. Links with cosine similarity below the threshold (e.g., below 0.65) are flagged as potentially incorrect or stale — a requirement was linked to a test case that, on semantic analysis, does not actually address the requirement's intent. These "weak link" flags are the most directly actionable output of the embedding traceability pipeline.

Semantic Coverage Analysis

Coverage analysis answers: "For each requirement, are there test cases in the test suite that semantically cover its specified behavior?" This is distinct from structural coverage (does a link exist?) and provides a ground-truth coverage estimate that reflects testing intent rather than database state.

• Full coverage: One or more test cases with cosine similarity ≥ 0.80 to the requirement. Requirement is semantically tested.
• Partial coverage: Test cases with similarity 0.65–0.80 exist. Coverage is possible but uncertain — human review required to confirm the test case actually tests the requirement behavior.
• Gap identified: No test cases with similarity ≥ 0.65. The requirement is likely not tested. This is a genuine traceability gap regardless of what the link database shows.

Duplicate and Conflict Detection

Within the requirements set itself, embedding analysis detects two critical conditions:

• Near-duplicates (similarity ≥ 0.90): Requirements expressing the same behavior in different language. Common in large programs where multiple authors write requirements without complete visibility of the existing corpus. Near-duplicates must be explicitly reconciled — either merged into a single authoritative requirement or documented as intentionally distinct with an explicit differentiation rationale.
• Potential conflicts (similarity 0.65–0.85, same domain cluster): Requirements that concern the same system aspect but specify behaviors that may be incompatible. For example, a performance requirement specifying maximum latency and a reliability requirement specifying redundancy behavior that necessarily introduces latency. Embedding analysis surfaces these as candidates; domain experts adjudicate whether a real conflict exists and how it is resolved.

Automated Impact Analysis

When a requirement changes, the impact analysis question is: what else changes? Traditional impact analysis traverses the explicit traceability graph — following links from the changed requirement to directly linked artifacts, then to artifacts linked to those, and so on. This graph traversal has two failure modes: it misses implicit impacts (artifacts semantically affected but not explicitly linked), and it amplifies noise (follows weak or incorrect links to irrelevant artifacts).

Embedding-driven impact analysis adds a semantic layer to graph traversal. When requirement R changes, the system:

1. Computes the embedding of the changed requirement and its predecessor version.
2. Measures the semantic delta between the two versions — the change's impact direction and magnitude in semantic space.
3. Identifies all artifacts (design elements, test cases, other requirements, code modules) within a semantic neighborhood of the changed requirement — not just explicitly linked ones.
4. Ranks the impacted artifacts by their cosine proximity to the semantic delta, distinguishing between artifacts that concern the aspect that changed versus aspects that were stable.
5. Generates a stratified impact report: Critical (high semantic proximity to the changed aspect), Potential (moderate proximity), and Review (low proximity but in the same domain cluster).

Change Propagation Modeling

For programs operating under a formal engineering change proposal process, embedding-driven impact analysis supports automated change propagation modeling: given a proposed requirement change, simulate the full downstream semantic impact before the change is approved, enabling the change control board to make informed decisions about the true cost and risk of the proposed change.

This capability is particularly valuable for MBSE programs using SysML models. The embedding pipeline can process both natural language requirements text and model element descriptions, identifying where a requirement change in the requirements management tool will likely propagate to specific blocks, interfaces, or constraints in the system model — enabling proactive model update planning before the change is implemented.

System Alignment Checking

System alignment is the highest-level application of embedding-driven requirements analysis: measuring whether the system being built — as described by its design documentation, test specifications, and implementation artifacts — remains semantically aligned with the stakeholder needs documented in the original requirements. Alignment checking operates at the program level, not the individual requirement level, and is designed to detect the systemic drift that emerges over long development programs.

The Alignment Measurement Framework

Alignment is measured along three axes, each capturing a different dimension of requirements-system correspondence:

Alignment Scoring

Each alignment axis is scored as a percentage: the fraction of requirement-artifact pairs above the semantic coverage threshold. An overall alignment score combines the four axes with weights calibrated to the program's current lifecycle phase — Axis 01 (stakeholder coverage) is most critical at requirements review; Axis 03 (temporal drift) becomes most critical during system integration testing. Programs below a defined alignment threshold trigger a formal re-alignment review.

Implementation Architecture

The embedding-driven requirements management system integrates with existing requirements management tooling (DOORS, Jira, Confluence, IBM Engineering Lifecycle Management, Polarion) without replacing it. The embedding pipeline processes artifact content, maintains a semantic vector store, and exposes analysis capabilities through APIs and dashboards — sitting alongside existing tooling as an analytical layer, not replacing the authoritative data store.

Security Considerations for Classified Programs

Requirements for classified defense programs contain sensitive technical information that cannot be processed by commercial embedding APIs without appropriate data handling controls. For classified programs, the architecture deploys on-premises or in a government cloud enclave using open-weight embedding models (e.g., a fine-tuned Llama or Mistral variant) that process requirements text without any data leaving the classified environment. The vector store operates within the classification boundary; only aggregated analysis outputs (scores, gap counts, conflict flags) are surfaced to unclassified reporting dashboards. This architecture is directly aligned with the Secure RAG design patterns documented in Continuum's CR-04 publication.

Interactive Traceability Explorer

The following demonstration simulates embedding-based traceability analysis for a representative set of satellite communication system requirements. Select a requirement to see its semantic traceability links across design elements, test cases, and peer requirements — scored by cosine similarity.

Interactive Impact Analysis Tool

The following tool simulates automated change impact analysis. Select a requirement that has been modified to see which downstream artifacts are semantically impacted, stratified by impact criticality. This reflects the output of the embedding-driven impact engine applied to a representative defense system requirements set.

MBSE & DoD Integration

Model-Based Systems Engineering (MBSE) and embedding-driven requirements management are not competing approaches — they are complementary. MBSE provides the formal modeling infrastructure (SysML blocks, interfaces, constraints, behavioral diagrams); embedding-driven analysis provides the semantic bridge between natural language requirements artifacts and formal model elements that no prior technology has adequately addressed.

The Natural Language to Model Gap

The persistent challenge in MBSE adoption is the gap between natural language stakeholder needs and formal model elements. Stakeholders express needs in natural language. Systems engineers translate those needs into formal requirements. MBSE practitioners translate formal requirements into model elements. At each translation step, semantic content can be lost, distorted, or left ambiguous. Embedding analysis at each translation boundary — stakeholder needs to requirements, requirements to model elements — provides a quantified semantic fidelity metric that validates whether the translation preserved intent.

Cameo Systems Modeler Integration

Continuum's MBSE practice uses Cameo Systems Modeler as the primary modeling tool for DoD programs. The embedding-driven requirements pipeline integrates with Cameo through the following workflow:

• Requirements text from DOORS or Jira is embedded and indexed nightly, with change detection flagging modified requirements.
• SysML model element descriptions (block names, interface definitions, constraint notes, use case descriptions) are extracted from Cameo via API and embedded in the same vector space.
• The coverage analyzer computes semantic similarity between each requirement and its linked model elements, surfacing requirements with low model coverage scores for MBSE team review.
• When a requirement changes, the impact engine identifies affected model elements by semantic proximity — enabling MBSE engineers to proactively update the model rather than discovering the gap at the next review gate.

INCOSE Requirements Quality Metrics

INCOSE's Guide to Writing Requirements defines quality attributes for well-formed requirements — unambiguous, verifiable, traceable, consistent, complete. Embedding analysis can automate the measurement of several of these attributes:

• Consistency (automated): Near-duplicate and conflict detection identifies requirements that violate consistency. Score: fraction of requirement pairs with similarity 0.65–0.85 that are flagged for potential conflict.
• Completeness (automated): Coverage gap analysis identifies requirements without semantic traceability to test cases. Score: fraction of requirements with full semantic coverage.
• Traceability (automated): Link validation score measures the fraction of existing links that have cosine similarity above the validation threshold.
• Verifiability (semi-automated): Requirements that are too abstract to embed meaningfully (very short, highly generic) are flagged for human review of their testability.

Evaluation & Metrics

Embedding-driven requirements management must itself be evaluated against ground truth to validate that the semantic similarity scores are accurately reflecting real traceability relationships. The evaluation methodology compares embedding-based link recommendations against expert human judgments across a sample of the requirements corpus.

Evaluation Methodology

Continuum's evaluation protocol for requirements embedding pipelines follows a structured validation process:

1. Expert panel construction: A panel of 3–5 domain experts (systems engineers with deep knowledge of the program) independently rates traceability link quality for a stratified random sample of requirement-artifact pairs. The inter-rater agreement (Fleiss' kappa) establishes the ground truth reliability ceiling.
2. Precision and recall measurement: For the sampled requirement-artifact pairs, compare embedding-based recommendations (above the cosine threshold) against expert judgments. Precision measures the fraction of embedding-recommended links that experts confirm; recall measures the fraction of expert-confirmed links that the embedding pipeline identified.
3. Threshold calibration: Adjust the cosine similarity threshold to optimize the precision-recall trade-off for the specific program's risk tolerance. High-risk programs (safety-critical, classified) may prefer higher thresholds (higher precision, lower recall) to minimize false positives; resource-constrained programs may prefer lower thresholds.
4. Drift monitoring: Repeat the evaluation on 5% of the corpus quarterly to detect model accuracy drift as the requirements corpus evolves.

Benchmark Performance Results

Results from Continuum pilot programs on aerospace and DoD program requirements corpora. Domain calibration uses fine-tuning on INCOSE-aligned requirements and program-specific terminology. Expert panel ground truth with kappa ≥ 0.72.

Toolchain & Integration Guide

The embedding-driven requirements management system integrates with the existing toolchain rather than replacing it. The following reference architecture specifies integration points, recommended components, and configuration guidance for common defense program tool stacks.

Integration with Existing Requirements Workflows

The embedding pipeline integrates as a non-disruptive analytical overlay on top of existing requirements management processes. Requirements engineers continue working in their existing tools (DOORS, Jira, ELM). The embedding pipeline runs nightly, processes any changes since the last run, updates the semantic index, and surfaces new analysis findings in the dashboard and tool plugin. There is no change to the authoritative data source; the embedding system is an advisory analytical layer, not an authoritative one.

• Existing link graph is preserved and augmented — no links are deleted; new semantic links are suggestions, not authoritative links
• All embedding recommendations include an explanation generated by the LLM explanation layer — requirements engineers see why the system suggested a link, not just that it did
• Human approval required to create, modify, or delete any authoritative traceability link — the embedding system is advisory, never autonomous for requirements data
• Full audit trail of all embedding recommendations and human accept/reject decisions — required for INCOSE-compliant requirements management process documentation

Implementation Roadmap

Implementing embedding-driven requirements management is a phased investment. Programs should resist the temptation to deploy all capabilities simultaneously — the analysis outputs are only as valuable as the requirements corpus quality, and early phases of the roadmap improve that quality while building team familiarity with the tool's outputs.

The Continuum Approach

This paper describes the published research and operational methodology that underpins Continuum's requirements engineering practice. Kurt A. Richardson, PhD, developed the embedding-driven requirements management framework as part of Continuum's R&D program — the research that produced this white paper is the same research that informs our MBSE engagements on active DoD programs. We do not offer requirements management consulting based on frameworks we have read about; we offer it based on frameworks we have built, tested, and refined through operational delivery.

Engagement Models

Conclusion

Requirements management has been a manual, labor-intensive discipline by necessity — the tools available have not supported the kind of semantic analysis that the problem actually requires. Semantic embeddings change the economics and the capability. What once required weeks of expert review time — checking whether a large set of requirements are consistently covered, identifying all potential impacts of a proposed change, measuring whether the system is semantically aligned with its requirements baseline — can now be accomplished in minutes with a well-calibrated embedding pipeline.

The result is not the elimination of requirements engineering expertise — it is the redirection of that expertise toward the decisions only humans can make, freed from the mechanical work of traversing large traceability corpora that embedding analysis can perform automatically, comprehensively, and consistently. The systems engineer who once spent 30% of their time maintaining traceability links can now spend that time evaluating the semantic conflicts the embedding system surfaces — which is the work that actually prevents system failures.

References

• [INCOSE-2023] International Council on Systems Engineering — "Guide to Writing Requirements (GtWR)" — INCOSE-TP-2010-006-04, 2023. The authoritative INCOSE requirements quality criteria applied in alignment assessment.
• [INCOSE-HDBK] International Council on Systems Engineering — "Systems Engineering Handbook v4.0" — 2015. SE process framework within which embedding-driven RE operates.
• [HAYES-2006] Hayes, J.H., Dekhtyar, A., Sundaram, S.K. — "Advancing Candidate Link Generation for Requirements Tracing" — IEEE Transactions on Software Engineering, 32(1), 2006. Foundational research on IR-based traceability, predecessor to embedding approaches.
• [SEN-2020] Sentilles, S., Gorschek, T. et al. — "Quality of Requirements Traceability in Industrial Practice" — IEEE Software, 2020. Empirical study of traceability quality failures in industrial programs.
• [REIMERS-2019] Reimers, N. & Gurevych, I. — "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks" — EMNLP 2019. Foundation paper for the sentence-level embeddings used in requirements analysis.
• [BOGART-2023] Bogart, M. et al. — "LLM-Augmented Requirements Tracing: A Systematic Evaluation" — REFSQ 2023. Evaluation of large language model approaches to requirements traceability, validating cosine similarity thresholds.
• [MUSTAFA-2022] Mustafa, N., Labiche, Y., Towey, D. — "A Systematic Literature Review of Requirements Traceability" — IEEE Access, 2022. Comprehensive review of traceability techniques including embedding approaches.
• [DBLOCH-2021] D'Addio, R. et al. — "Automated Traceability Link Recovery using Fine-tuned BERT Models" — ICSME 2021. Domain fine-tuning methodology applied in CDMF calibration process.
• [CR-01] Richardson, K.A. — "WP-CR-2025-01: Agentic AI in Mission-Critical Environments" — Continuum Resources, 2025. Agentic architecture for automated requirements analysis workflows.
• [CR-02] Richardson, K.A. — "WP-CR-2025-02: Fine-Tuning vs. RAG Decision Framework" — Continuum Resources, 2025. Technical basis for the embedding model selection and domain calibration methodology.
• [CR-04] Richardson, K.A. — "Secure RAG Architectures" — Continuum Resources, 2024. Security architecture for the classified requirements embedding deployment described in Section 08.
• [DOD-5000-87] Department of Defense — "Operation of the Software Acquisition Pathway" — DoDI 5000.87, 2020. Requirements traceability obligations for programs under the Software Acquisition Pathway.
• [SERC-2019] Systems Engineering Research Center — "Requirements Engineering in DoD System Acquisitions" — SERC-2019-TR-108, 2019. Defense-specific requirements engineering practice and failure mode analysis.