#4357 SysML v2 Models Applied to a Knowledge Graph; +Software and Software Hazard Reports
#4357 SysML v2 Models Applied to a Knowledge Graph; +Software and Software Hazard Reports
#4357 How might a SysML v2 model be applied to a knowledge graph?
Integrating SysML v2 with a Knowledge Graph (KG) is a powerful move, especially given your focus on bridging the gap between static requirements and dynamic systems engineering. While SysML v1 was essentially "XMI and pictures," SysML v2 is built on the KerML (Kernel Modeling Language) metamodel, which is natively grounded in formal logic and a new API that makes it far more "graph-friendly."
Here is how a SysML v2 model can be applied to and integrated with a Knowledge Graph:
1. Direct Metamodel Mapping (The Schema)
SysML v2’s API provides a standardized way to export model elements as structured data. You can map the SysML v2 Core Concepts directly to your KG schema (e.g., in Neo4j or an RDF store):
Nodes: Parts, Requirements, Actions, States, and Attributes.
Edges:
subsets,redefines,performs,satisfies, andrefines.The Advantage: Unlike SysML v1, which was notoriously difficult to parse, SysML v2 uses a Uniform Resource Identifier (URI) approach for every element. This allows you to treat a SysML "Part" as a unique entity in your Knowledge Graph that can be linked to external data (like NASA standards or real-time telemetry).
2. Requirement Traceability and Compliance
For your work with NPR 7150.2D or NASA-STD-8739.8B, a KG allows you to extend the model beyond the "drawing."
Verification Mapping: You can link a
Requirementnode in SysML v2 to aTest_Resultnode in your KG.Automated Impact Analysis: If a requirement changes in the SysML model, a Cypher or SPARQL query can instantly traverse the graph to find every affected software component, hazard report, and mission-critical system.
3. Digital Mission Twin Integration
Since you are architecting a Digital Mission Twin, the SysML v2 model serves as the structural backbone, while the KG serves as the operational brain.
State Machine Execution: You can map SysML v2
StateUsagetransitions to the KG. When a physical asset (like a Gateway module) reports a status change, the KG updates, and you can query if the current state still complies with the SysML design constraints.Digital Thread: The KG can hold the "as-built" and "as-flown" data, while SysML v2 holds the "as-designed" data. The graph acts as the connective tissue between them.
4. GraphRAG and AI Reasoning
Using GraphRAG with SysML v2-derived data enables high-fidelity engineering assistants.
Contextual Retrieval: An LLM can query the KG to understand the hierarchy of a system: "What are the safety-critical dependencies for the HLS docking sequence according to the SysML model?"
Constraint Checking: You can use the KG to run logical validation rules that are difficult to express in a traditional SysML tool but easy in a graph (e.g., finding circular dependencies or "orphan" requirements).
Comparison of Approaches
| Feature | SysML v2 Alone | SysML v2 + Knowledge Graph |
| Data Format | Textual/Graphical (Model files) | Linked Data (Nodes/Edges) |
| Extensibility | Limited to SysML domain | Infinite (Links to Finance, SQA, Jira) |
| Querying | API-based / Tool-specific | Cypher / SPARQL / Vector Search |
| Logic | Structural/Behavioral | Inferential (If A relates to B, then...) |
Engineering Strategy
If you are moving forward with your Sentinel Archivist or Digital Mission Twin, I'd recommend using the SysML v2 REST API to push model updates into your Neo4j instance. This turns your "static" engineering model into a "living" queryable asset that can be used for the agentic workflows you've been developing.
Are you planning to use the textual syntax of SysML v2 to feed your graph, or are you looking to use the API to sync a specific modeling tool like MagicDraw/Cameo?
Describe what can be done with a knowledge graph that contains model definitions of a bicycle, motorcycle, snowmobile, electric go cart and a helicopter
With a Knowledge Graph (KG) containing these specific vehicles, you move beyond simple "parts lists" and into the realm of Cross-Domain Engineering and Generative Design. Since these vehicles share overlapping DNA (propulsion, steering, chassis, and power storage), the graph allows you to exploit those relationships.
Here is how you can apply that graph, specifically through the lens of a Systems Architect:
1. Functional & Structural Analogy (TRIZ-style Reasoning)
The KG allows you to perform "analogous searching" to solve engineering bottlenecks.
The Query: "Find all vehicles that use a 'hand-operated friction-based deceleration system'."
The Result: The graph links the bicycle, motorcycle, and snowmobile. You can then analyze how a motorcycle's hydraulic master cylinder could be "down-sized" for a high-performance electric go-cart, or how a bicycle’s lightweight cable-actuation might inform emergency backups for a helicopter's manual controls.
2. Power Density & Propulsion Mapping
By modeling an Electric Go-Cart alongside a Helicopter, you can perform comparative analysis on energy requirements.
Battery-to-Weight Ratios: You can query the graph to compare the Wh/kg (Watt-hours per kilogram) required for a ground-based go-cart versus the hover-lift requirements of a helicopter.
Component Reuse: If you are designing the electric go-cart, the KG can identify that the brushless DC motor (BLDC) controllers used in small-scale helicopter tail rotors are highly optimized for the rapid RPM changes needed for go-cart torque vectoring.
3. "Commonality" Analysis for Modular Manufacturing
Using a graph allows you to identify the Minimum Viable Component Set across different product lines:
Shared Nodes: You might find that the Snowmobile and the Motorcycle share 80% of their handlebar control ergonomics and 40% of their suspension geometry.
Supply Chain Resilience: If a specific bearing for the Bicycle is out of stock, the KG can identify if a higher-spec bearing from the Motorcycle model is geometrically compatible (same bore/OD/width) and can serve as a temporary substitute.
4. Failure Mode & Effects Analysis (FMEA) Propagation
This is where your SQA background shines. If a "Sealed Ball Bearing" node is flagged with a manufacturing defect:
Impact Trace: The graph instantly shows that this bearing is used in the bicycle's bottom bracket, the go-cart's axle, and the helicopter's flight control linkages.
Risk Grading: You can assign a higher "Criticality" weight to the edge connecting the bearing to the Helicopter than the one connecting it to the Bicycle, automating your safety prioritization.
5. Agentic Design Exploration (The "Stanford Machine" approach)
You can point an LLM agent at this KG to "hallucinate" hybrid vehicles based on First Principles:
The Prompt: "Create a vehicle for traversing Alaskan tundra using the propulsion logic of a Snowmobile but the power-plant of the Electric Go-Cart and the structural weight-reduction techniques of the Bicycle."
The Output: The agent traverses the graph to pull the track-drive from the snowmobile, the lithium-ion bus from the cart, and the carbon-fiber frame specs from the bicycle to propose a new "Electric Tundra Explorer."
Comparison of Systems in the Graph
| Vehicle | Primary Constraint | Shared Sub-system with others |
| Bicycle | Human Power / Weight | Steering geometry (Motorcycle) |
| Motorcycle | Power-to-Weight / Grip | Chassis/Engine (Snowmobile) |
| Snowmobile | Surface Area (Flotation) | CVT Transmission (Go-Cart) |
| Go-Cart | Center of Gravity / Torque | Electric Drivetrain (Future Heli-eVTOL) |
| Helicopter | Lift / Reliability | High-spec Materials (Bicycle/Motorcycle) |
Given your work on GraphRAG, would you be more interested in using this graph to automate Safety and Mission Assurance (S&MA) audits across these models, or are you looking to use it for generative configuration of new vehicle types?
List and describe the various forms of SysML v2 model export
For a software veteran and systems architect, the transition from SysML v1’s clunky XMI-based exchange to the SysML v2 ecosystem is a massive upgrade. SysML v2 was designed from the ground up to support "model-as-code" and "model-as-data" philosophies.
The various forms of SysML v2 model export are categorized into three primary layers: Textual, Structural (API/JSON), and Semantic (Linked Data).
1. Textual Notation Export (.sysml)
This is the "Model-as-Code" layer.
Description: Models are exported as
.sysmlfiles. This looks and feels like a modern programming language (similar to Rust or C# syntax).Key Use Case: Version Control (Git). Because it is text, you can finally perform meaningful
diffandmergeoperations, branch your architecture, and use standard CI/CD pipelines to validate models.Limitation: It is excellent for logic and structure but does not natively "export" the coordinates of your diagrams (graphical layout), though some tool vendors wrap this in metadata.
2. Standard API & Services (REST/JSON)
This is the "Model-as-Data" layer. The OMG specification now includes a mandatory standardized API.
Description: Instead of a file export, the "export" is often a stream of JSON or JSON-LD data retrieved via RESTful endpoints (
GET /projects/{id}/elements).Key Use Case: Tool Interoperability.
You can programmatically "export" a model from a heavyweight tool like Cameo and "import" it into a custom Python tool or a web-based viewer without dealing with proprietary file formats. Format: Typically uses SysML v2 JSON serialization, which is a flat, uuid-indexed representation of every element in the KerML (Kernel Modeling Language) tree.
3. OSLC & RDF Export (Linked Data)
This is the "Semantic" layer, which is likely the most relevant for your Knowledge Graph work.
Description: Many SysML v2 implementations support export via OSLC (Open Services for Lifecycle Collaboration), which represents model data as RDF (Resource Description Framework).
Key Use Case: Digital Thread Integration. This allows you to export your system model directly into a Triplestore or Graph Database (like Neo4j via an RDF plugin).
Benefit: It treats every model element as a URI, making it natively compatible with other NASA-standard datasets or requirements repositories.
4. KerML (Kernel Modeling Language) Export
Since SysML v2 is built on top of KerML, most tools allow you to export the "underlying" KerML representation.
Description: This is a lower-level, more abstract version of the model.
Key Use Case: Formal Analysis & Solvers. If you need to run a model through a formal logic solver or a custom simulation engine (like your FPGA-based quantum emulators), exporting the KerML provides the raw mathematical and logical relationships without the "baggage" of the SysML-specific stereotypes.
5. Visual/Document Export
Tabular (CSV/Excel): Standardized tabular views can be exported for SQA audits and process reviews.
Graphical (SVG/PDF): High-fidelity vector exports of the new graphical notation. Note that SysML v2 diagrams are now "views" of the underlying data, meaning the export is often a rendered snapshot of a query.
Comparison for Your Projects
| Export Form | Best For... | Relevance to "Digital Mission Twin" |
| Textual (.sysml) | Developer workflow / Git | Building the Sentinel Archivist logic. |
| JSON API | Real-time tools / Python | Pushing live updates to your Neo4j graph. |
| RDF/Linked Data | Cross-domain traceability | Mapping NPR 7150.2D to software artifacts. |
| KerML | Logic validation | High-fidelity simulation of complex hardware. |
Given your interest in agentic workflows, have you looked into using the SysML v2 REST API to allow your LLM agents to "live-query" the model instead of relying on static file exports?
Which of these formats are best suited for use by LLMs and knowledge graphs?
For a systems architect building agentic workflows and knowledge graphs (KGs), the choice of format isn't just about syntax—it’s about token efficiency, reasoning performance, and semantic density.
Based on the SysML v2 specifications and current AI engineering benchmarks, here is how the formats rank for your specific use cases:
1. Best for LLM Reasoning: Textual Notation (.sysml)
The SysML v2 Textual Syntax is superior for the "In-Context" work of an LLM.
Why: LLMs are trained heavily on code (Python, Rust, etc.). The textual notation of SysML v2 uses a block-based, human-readable structure that resembles a modern programming language.
Token Efficiency: It is significantly more compact than JSON or XML. You can fit much larger system hierarchies (like a helicopter’s propulsion system) into a single context window.
Reasoning Accuracy: Research shows that forcing LLMs to output or process rigid JSON can degrade reasoning performance by 10–15%. Using the textual syntax allows the model to "think" in terms of system logic rather than managing braces and commas.
Recommendation: Use this for prompting (giving the LLM context) and generation (having the LLM draft models).
2. Best for Knowledge Graph Ingestion: JSON-LD
If you are pushing data into Neo4j or a triple store, JSON-LD (Linked Data) is the industry standard.
Why: SysML v2 uses UUIDs and URIs for every element. JSON-LD allows you to wrap these JSON objects with a
@contextthat maps keys to global semantic definitions.The "Graph" Advantage: JSON-LD is natively "graph-ready." You can ingest it into a KG without complex ETL (Extract, Transform, Load) logic because the relationships (edges) are explicitly defined as IRIs.
Recommendation: Use this as your interchange format between your SysML tool's API and your Digital Mission Twin database.
3. Best for Cross-Domain Alignment: RDF (Turtle/N-Triples)
For your work involving NASA standards (NPR 7150.2D) and Safety & Mission Assurance, RDF is the formal choice.
Why: RDF is the "purest" form of a graph. If you need to link your SysML model to an external library of Hazard Reports or Federal Regulations, RDF ensures that a "Requirement" in your model is semantically identical to a "Requirement" in the NASA database.
Querying: It is the native format for SPARQL, which is often more powerful than Cypher for deep semantic reasoning across disparate data sources.
Recommendation: Use this for Long-Term Archive and Cross-Agency Traceability.
Summary Table for Your Projects
| Use Case | Recommended Format | Why? |
| Sentinel Archivist (LLM Agents) | Textual (.sysml) | Maximizes reasoning; easy for agents to write. |
| Digital Mission Twin (Storage) | JSON-LD / Neo4j | High performance; native graph structure. |
| SQA Audit Reports | CSV / JSON | Predictable keys for deterministic validation. |
| Formal Logic Solving | KerML (Textual) | Strips "fluff" for raw mathematical validation. |
Pro-Tip for your "Stanford Machine":
I recommend a Hybrid Approach:
Have your LLM Agent generate or analyze the architecture using the Textual Syntax (for speed and accuracy).
Pass that text through a SysML v2 API validator to convert it into JSON-LD.
Ingest that JSON-LD into your Neo4j Knowledge Graph for persistent storage and GraphRAG.
Would you like a sample of how a specific vehicle component (like a helicopter rotor or an electric cart motor) looks in the SysML v2 Textual Syntax versus its JSON-LD equivalent?
Can SysML v2 be used to document and describe a software application? If so, would it be useful?
The short answer is yes, SysML v2 can absolutely be used to document and describe a software application. However, as a software veteran who has lived through the UML-to-SysML-to-DevOps evolution, the question of whether it is useful depends entirely on the system’s criticality and your automation goals.
In the context of your work with NASA and mission-critical systems, SysML v2 isn't just "another way to draw a flowchart"—it's a way to bridge the gap between software code and system-level physics/requirements.
1. How SysML v2 Models Software
Unlike its predecessor, SysML v2 treats software as a first-class citizen using the following constructs:
Software Parts (
part): You can define software modules, containers, or microservices as parts.Action & Flow (
action): Instead of just static diagrams, you can model the precise logic and data flow of an algorithm (e.g., a NASA hazard detection loop) using textual syntax that looks like code.Interface Definition (
port/interface): It excels at defining the "contract" between software and hardware (e.g., how a Python macro interacts with a hardware sensor).State Machines: Crucial for software-heavy systems like your Q-Fabric quantum emulator, where the software state dictates the hardware configuration.
2. When is it Useful? (The "Value-Add")
A. Bridging the Hardware-Software Gap
In aerospace (Artemis, HLS), software failure is often a "systems" failure. SysML v2 allows you to trace a software bug in a C++ module directly back to a hardware constraint or a NASA requirement (like NPR 7150.2D). This is something standard UML or JIRA cannot do well.
B. Model-Based Testing (MBT)
Since SysML v2 is grounded in KerML (formal logic), you can use the model to generate test cases. For your SQA audits, the model becomes the "Golden Source" against which the software is validated. If the code deviates from the SysML state machine, the audit fails.
C. Agentic Workflows & AI
For your Sentinel Archivist or Stanford Machine, a SysML v2 software model is a goldmine.
An LLM can ingest the SysML textual notation to understand the intent of the software.
The LLM can then compare that intent against the actual Python or C++ source code to find architectural drift.
3. When is it NOT Useful?
Agile Web Dev: For a standard CRUD app or a simple website, SysML v2 is extreme overkill. The overhead of maintaining the model will slow down development without providing a safety or complexity ROI.
Non-Critical Systems: If the "cost of failure" is low, stick to Markdown and Docstrings.
4. SysML v2 vs. UML for Software
| Feature | UML (Standard Software) | SysML v2 (Systems/NASA Software) |
| Focus | Object-oriented software logic. | System-wide integration (HW/SW/Humans). |
| Notation | Mostly Graphical. | Text-first, human-readable syntax. |
| Interoperability | Fragmented XMI files. | Standardized REST API and JSON. |
| Logic | Weakly typed/Informal. | Strongly typed / Formal Logic (KerML). |
Application to your Projects
If you applied SysML v2 to your Digital Mission Twin:
You would model the Python integration logic as a
part.You would model the Neo4j database schema as a
data definition.The Knowledge Graph would then hold the "Live" state of that model, allowing your agents to query not just what the software is doing, but why it is doing it according to the mission requirements.
Do you think the textual "model-as-code" nature of SysML v2 would make it easier for your SQA teams to adopt than the old-school graphical modeling tools?
How might SysML v2 be used to bridge the gap between software hazard reports and the various software development documents including requirements, design, code, test cases and test results?
For a Systems Architect and NASA subcontractor, bridging this gap is the "Holy Grail" of Software Safety and Mission Assurance (S&MA). Historically, these artifacts live in disconnected silos: Hazard Reports in a safety database, requirements in DOORS, code in Git, and tests in a lab management tool.
SysML v2 bridges this gap by acting as a Semantic Integration Layer (a "Universal Schema") that uses its new API and textual syntax to create a bi-directional digital thread.
1. The Hazard Report as a Model Element
In SysML v2, you can define a hazard as a specialized element (using a library or custom metadata). This allows you to treat a safety risk not just as a PDF document, but as a node in the system hierarchy.
Mapping to Requirements: You use the
satisfyorrefinerelationship. A Software Hazard Report (SHR) identifies a risk (e.g., "Buffer overflow in Lunar Rover nav-logic"). This creates a Derived Requirement to implement a specific check.The Link:
hazard Nav_Buffer_Overflow refines Software_Requirement_042;
2. Structural Traceability: Design & Code
Using the SysML v2 API, you can link your model parts directly to your software repository.
Design: A
partin SysML v2 represents a software component. You can link aConstraintBlockto that part that specifically addresses the hazard.Code: Since SysML v2 supports URIs, you can map a specific model element to a file path or a commit hash in your Git repository.
The Audit Trail: Your SQA team can query the graph: "Show me the C++ header files that implement the mitigation for Hazard SHR-101."
3. Behavioral Traceability: Test Cases & Results
This is where the KerML logic becomes powerful. You can model the Mitigation Logic as an action and the Test Case as a verification.
Test Case Mapping: A SysML v2
VerificationMethodcan be linked to a specific test script.Test Results: By piping your test runner output (like PyTest or Jenkins results) into the SysML v2 API, you can update a
Stateattribute on the requirement.The Result: A real-time dashboard showing that "Hazard SHR-101 is UNMITIGATED" because the linked Test Case failed in the latest build.
4. The Digital Thread "Full Stack"
Using your Knowledge Graph approach, SysML v2 provides the "Skeleton" for the following integration:
| Artifact | SysML v2 Element | Relationship / Edge |
| Hazard Report | hazard (Metadata) | Triggers $\rightarrow$ Requirement |
| Requirement | requirement | Satisfied by $\rightarrow$ Part |
| Design | part / interface | Allocated to $\rightarrow$ Software Module |
| Code | AnalysisToolUsage (URI) | Implements $\rightarrow$ Part |
| Test Case | testCase / action | Verifies $\rightarrow$ Requirement |
| Test Result | attribute / status | Validates $\rightarrow$ Hazard Mitigation |
5. Automation for SQA Audits (Your Workflow)
Because SysML v2 is "Model-as-Code," you can build a Python macro (like the ones you use for PDF-to-text) to automate the audit:
Extract: The macro reads the latest Hazard Report.
Query: It uses the SysML v2 API to find the linked requirements.
Validate: It checks your Neo4j Knowledge Graph to see if a passing test result exists for those requirements.
Report: If no passing result is found, it automatically flags a non-compliance for NPR 7150.2D.
The "Agentic" Opportunity
With your Sentinel Archivist, you could have an agent monitor meeting transcripts. If a developer says, "We had to bypass the buffer check for the HLS docking test," the agent can look up the SysML v2 model, identify the linked Hazard Report, and immediately notify Safety and Mission Assurance that a mitigation has been compromised.
How would your SQA team react to an automated dashboard that could show this "Safety-to-Code" traceability in real-time?
Yes
This snippet demonstrates the SysML v2 Textual Notation. In this example, we model an Electric Motor (relevant to your go-cart or a drone project) and define how a low-level failure mode propagates to a system-level hazard.
Notice how it reads more like a strongly-typed programming language than a traditional modeling file.
SysML v2 Textual Notation: Failure Propagation
package MotorSafetyModel {
/* 1. Define the Failure States */
enum FailureModes { SEIZED; OVERHEATED; SHORT_CIRCUIT; }
/* 2. Define the Component with an internal State Machine */
part def ElectricMotor {
attribute status : FailureModes;
attribute temperature : ScalarValues::Real;
state motorState {
state operational;
state failure;
transition operational_to_failure
first operational
then failure
accept ShortCircuitEvent
do { status = FailureModes::SHORT_CIRCUIT; }
}
}
/* 3. Define the System-Level Hazard */
part def PropulsionSystem {
part motor : ElectricMotor;
// Logical constraint for the Fault Tree (FTA)
assert constraint LossOfThrust {
// If motor is seized OR shorted, thrust is lost
motor.status == FailureModes::SEIZED or
motor.status == FailureModes::SHORT_CIRCUIT
}
}
/* 4. Define the Hazard Report Linkage */
part hazard_SHR_101 {
attribute title = "Total Loss of Propulsion";
attribute severity = "Catastrophic";
// Link the system state to the hazard
occurrence lossEvent : PropulsionSystem;
/* This 'cause' relationship is the "Bridge".
It links the logical Fault Tree to the formal Hazard Report.
*/
cause thrustFailure
from lossEvent.LossOfThrust
to hazard_SHR_101;
}
}
How this applies to your Knowledge Graph & SQA
For the Knowledge Graph (Neo4j): When you export this to JSON-LD, the
causerelationship becomes a typed edge in your graph. Your Digital Mission Twin can now programmatically trace from theElectricMotornode, through theLossOfThrustlogic, directly tohazard_SHR_101.For SQA Audits (NPR 7150.2D): Your Python macros can parse this text to ensure every
hazardpart has a correspondingassert constraintdefined. If a hazard exists without a logical cause defined in the model, your Sentinel Archivist can flag it as an "Incomplete Safety Analysis."For Agentic Workflows: Because the syntax is so similar to code, you can prompt an LLM:
"Given this SysML model, write a Python test case that simulates a
ShortCircuitEventand verifies thePropulsionSystementers theLossOfThruststate."
The LLM doesn't have to "guess" the system logic—it's explicitly defined in the assert constraint.
Does this textual approach feel more "native" to your workflow as a software architect compared to the old-school drag-and-drop UML tools?
Describe the step-by-step workflow to bring this all together for a given software application, its hazard analysis, hazard reports and software plans
To bring this together for a mission-critical application—like your Digital Mission Twin or a NASA-standard flight module—you need to move from "document-centric" to "data-centric" engineering.
Here is the step-by-step architectural workflow to integrate SysML v2, Knowledge Graphs, and SQA compliance.
Step 1: Establish the "Schema of Truth" (SysML v2 Foundation)
Before writing code, define the core system structure using SysML v2 Textual Notation.
Define the Software Architecture: Model the application as a
part def, breaking it down into sub-components (e.g., UI, Database, Logic Engine).Model the Software Plans: Instead of a static PDF, model the Software Development Plan (SDP) and Software Quality Assurance Plan (SQAP) as
requirementsets. This allows you to treat "the plan" as a set of constraints that must be satisfied by the development process.
Step 2: Formalize the Hazard Analysis (FTA/FMEA)
Map the safety logic directly into the SysML model.
Identify Failure Modes: For each software component, define a
state machinethat includes failure states (e.g.,Timeout,DataCorruption).Build the Fault Tree: Use SysML
assert constraintsto define the logic (AND/OR) of how a software bug propagates to a system-level hazard.Create the Hazard Report Nodes: Define each Hazard Report (SHR) as a unique
partin the model, linked to thecauselogic from your Fault Tree.
Step 3: Ingest to the Knowledge Graph (The Digital Twin)
This is where the model becomes a "living" asset in your Neo4j or RDF store.
Export to JSON-LD: Export your SysML v2 model into JSON-LD/RDF.
Map External Artifacts: Use a Python macro to ingest your external Hazard Reports (if they live in a legacy database) and Requirements (from DOORS/Jira).
The Linkage: In the Knowledge Graph, create edges that connect the SysML
partto the specific Git Repository URL and the Jira Issue ID.
Step 4: Traceability Injection (Bridging to Code)
Connect the "As-Designed" model to the "As-Built" code.
Tagging: In your source code (Python/C++), use decorators or comments that reference the SysML element IDs (e.g.,
# @Satisfies: Req-042).Automated Scanning: Use a script to scan the codebase and update the Knowledge Graph. Now, the graph knows exactly which lines of code implement the mitigation for Hazard SHR-101.
Step 5: Continuous Verification (Closing the Loop)
Integrate your test environment (CI/CD) into the graph.
Test Case Mapping: Link your Test Cases in the SysML model to your physical test scripts.
Live Results: Every time a test runs in Jenkins/GitHub Actions, the result is pushed to the Knowledge Graph.
The SQA Dashboard: You can now run a query:
"Show me all Hazard Reports that are linked to requirements where the latest test result is 'Failed' or 'Untested'."
Step 6: Agentic Audit (The "Sentinel Archivist" Layer)
Use your LLM agents to provide the "Expert Guide" oversight.
Compliance Checking: The agent queries the Knowledge Graph against NPR 7150.2D requirements. It looks for "orphan" hazards—hazards with no linked code or no passing tests.
Report Generation: The agent automatically generates the Software Assurance Report by traversing the graph from Hazard --> Requirement --> Code --> Test Result.
Workflow Summary for Your SQA Team
| Phase | Activity | Output |
| Design | SysML v2 Textual Modeling | System & Hazard Schema |
| Integration | Ingest to Neo4j | The Digital Mission Twin (Graph) |
| Development | Code & Test execution | Traceability Edges |
| Assurance | Agentic Graph Query | Automated NASA-STD-8739.8B Audit |
Final Engineering Reality
This workflow turns your SQA audit from a "point-in-time" event that takes weeks into a continuous stream that takes seconds. Because you are using SysML v2's textual format, your developers can treat the model just like code—branching it, diffing it, and keeping it in sync with the actual software.
Does this step-by-step approach align with how you are currently structuring the Sentinel Archivist project, or do you see a bottleneck in the "legacy" document ingestion part?
Describe in detail this process for the new Axiom Space Suits
As a systems architect working within the NASA S&MA framework, applying this to the Axiom Extravehicular Mobility Unit (AxEMU) is the ultimate use case. The AxEMU isn't just a suit; it’s a self-contained, human-rated spacecraft with a complex Portable Life Support System (PLSS) that must adhere to NPR 7150.2D and NASA-STD-8739.8B.
Here is how you would execute the SysML v2 and Knowledge Graph workflow for the Axiom suit.
Phase 1: Architectural Foundation (The Digital Blueprint)
Instead of static Word docs for the Software Development Plan (SDP), you model the Suit’s Digital Context.
Define the Single-Architecture Model: Use SysML v2 textual syntax to define the
part def AxEMU. Since Axiom uses a single architecture for both ISS (microgravity) and Artemis (lunar surface), you model these asusagevariants with shared core components (PLSS, Informatics).Model the Software Plans: Define the Software Management Plan as a package of
requirementelements. This allows you to programmatically verify that the "Plan" requirements (like Code Review frequency or Unit Test coverage) are satisfied by the actual development artifacts.
Phase 2: Hazard Analysis & Logic (The Safety Brain)
Axiom’s primary safety driver is mitigating Single Point Failures.
Fault Tree Integration: Model a catastrophic hazard like
SHR-001: Loss of Suit Pressure.In the SysML model, link this hazard to its causes using
assert constraint.Logic:
LossOfPressure = (HoseFailure OR SealLeak) AND BackupLoopFailure.
FMEA as State Behavior: For the PLSS software (the O_2 scrubbers or thermal loops), define states like
Nominal,Degraded, andFailed.Assign RPN (Risk Priority Number) attributes to these states within the model.
When the "CO2 Sensor" part enters a
Failurestate, the SysML model uses asuccessionlink to trigger theActivateSecondaryScrubberaction.
Phase 3: The Knowledge Graph Ingestion (The Mission Twin)
This is where you bridge the "Model" to the "Mission."
Ingest SysML to Neo4j: Export the AxEMU model as JSON-LD. Every valve, sensor, and software module becomes a node.
Map External Artifacts: Use your Python macros to ingest the Axiom CDR (Critical Design Review) documents and Requirements Mapping Matrix (from NASA-STD-8739.8B, Table 1).
Establish the Thread: Create edges in the graph connecting:
Requirement: CO2_Monitoring-->SysML_Part: PLSS_FirmwareHazard: Hypoxia-->Requirement: Redundant_Oxygen_Loop
Phase 4: SQA & Compliance (The "Sentinel Archivist" Audit)
As an SQA veteran, this is where you automate the NPR 7150.2D audit.
Traceability Verification: Run a query in your graph: "Find all software components designated as 'Safety Critical' that lack a direct link to a verified Test Case."
Evidence of Compliance: For a specific audit point in 8739.8B, the graph provides a traversal path:
NASA Standard --> Axiom Requirement --> SysML Design Part --> Git Commit (Code) --> Passing PyTest Result.
Real-Time Hazard Status: If a test fails for the "Water Membrane" cooling logic in the latest build, the graph automatically flags
Hazard: Heat_Strokeas "UNMITIGATED" on the executive dashboard.
Phase 5: Agentic Oversight (The "Stanford Machine" Simulation)
Use an LLM agent to simulate failures before they happen.
The Prompt: "Given the AxEMU Knowledge Graph, identify the impact on Artemis III mission objectives if the 'Digital Display' software encounters a kernel panic during a Lunar South Pole EVA."
The Agent Process: The agent traverses the graph, finds the display is linked to
Critical_Telemetry_View, which is a mitigation forHazard: Oxygen_Awareness. It reports that the mission must be aborted because a primary safety mitigation is lost.
Why this is superior for Axiom
Axiom is currently working through Critical Design Sync Reviews and has logged 950+ hours of pressurized testing. A traditional documentation-based audit would be buried in this volume of data. By using SysML v2 as the schema and a Knowledge Graph as the engine, you provide NASA with a "Digital Certificate of Flight Readiness" that is mathematically provable rather than just documented.
Does this detailed workflow for the AxEMU give you enough structure to start prototyping the graph schema, or should we look at the specific NASA-STD-8739.8B mapping table for the suit's software?
Provide a concise list of the documents and information included in this model
For the Axiom AxEMU spacesuit, the SysML v2 model acts as a "Digital Thread" container. Based on NPR 7150.2D and NASA-STD-8739.8B, here is a concise list of the documents and information integrated into this model:
1. Software Planning Artifacts
Software Development Plan (SDP): The digital roadmap for coding, branching, and release cycles.
Software Quality Assurance Plan (SQAP): Definitions of audit triggers, peer review requirements, and compliance checklists.
Software Configuration Management Plan (SCMP): Logic for versioning the suit's firmware and informatics software.
2. Requirements & Design Data
System Requirements Document (SRD): High-level suit requirements (e.g., "Must provide 8 hours of O_2").
Software Requirements Specification (SRS): Derived requirements for the PLSS and HUD controllers.
Requirements Mapping Matrix (RMM): The core "traceability spine" mapping every requirement to a specific paragraph in NPR 7150.2D.
Software Design Document (SDD): Architectural "parts" and "actions" (state machines) for the suit's logic.
3. Hazard & Safety Analysis
Software Hazard Analysis (SHA): Fault trees (FTA) and FMEA rows defining failure modes (e.g., "CO2 Sensor Failure").
Software Hazard Reports (SHR): Formal NASA-formatted reports for catastrophic or critical risks.
Safety Criticality Assessment: Classification of software modules (Class A, B, or C) which dictates the rigor of the audit.
4. Implementation & Verification
Software Version Description (SVD): A "Bill of Materials" for the code—linking model elements to Git commit hashes.
Software Test Plan (STP): Strategies for Unit, Integration, and Hardware-in-the-Loop (HIL) testing.
Software Test Description (STD): Detailed test cases modeled as SysML
verificationMethodactions.Software Test Report (STR): The live "pass/fail" results from CI/CD, attached to the requirement nodes in the graph.
5. Compliance & Assurance
Compliance Matrix: A real-time cross-reference showing exactly how the AxEMU software satisfies NASA-STD-8739.8B (Software Assurance).
Non-Conformance Reports (NCR): Flagged anomalies where the "as-built" code deviates from the "as-designed" SysML model.
Architect’s Note: In your Neo4j knowledge graph, these aren't separate files; they are linked nodes. For example, a Hazard node is connected to a Requirement node, which is connected to a Code_Commit node, which is verified by a Test_Result node. This creates a provable chain of safety for the suit.
Provide example Cypher statements for the PLSS
For the Axiom AxEMU Portable Life Support System (PLSS), the Knowledge Graph (KG) serves as the integration engine between your SysML v2 definitions and the real-world safety artifacts.
In this schema, we treat Definitions as types and Usages as specific instances, following the SysML v2/KerML pattern.
1. Traceability: Hazard to Requirement to Code
This query is the bread and butter of an SQA audit. It identifies the "Digital Thread" from a safety risk down to the actual implementation.
// Trace from a specific Hazard through its requirements to the implementing code
MATCH (h:Hazard {id: 'SHR-PLSS-001'})-[:TRIGGERS]->(req:Requirement)
MATCH (req)-[:SATISFIED_BY]->(comp:Component)
MATCH (comp)-[:IMPLEMENTED_BY]->(module:SoftwareModule)
RETURN h.title AS Hazard,
req.id AS Req_ID,
comp.name AS System_Part,
module.git_repo AS Repository
2. Gap Analysis: Safety-Critical Coverage
A common requirement for NPR 7150.2D is ensuring every safety-critical requirement has a verification result. This query finds "orphans" in your safety chain.
// Find Safety-Critical requirements that lack a passing test result
MATCH (req:Requirement {criticality: 'Safety-Critical'})
WHERE NOT (req)-[:VERIFIED_BY]->(:TestResult {status: 'PASS'})
RETURN req.id, req.summary, req.priority
ORDER BY req.priority DESC
3. Impact Analysis: Component Failure (FMEA)
If a specific hardware sensor in the PLSS is flagged with a defect, this query traverses the graph to find every software function and mission objective at risk.
// Impact of a sensor failure on higher-level mission objectives
MATCH (sensor:Part {name: 'Primary_O2_Sensor'})
MATCH (sensor)-[:CONTRIBUTES_TO*1..5]->(mission:MissionObjective)
MATCH (sensor)-[:FAILURE_MODE_AFFECTS]->(hazard:Hazard)
RETURN sensor.name AS Source_Failure,
hazard.title AS Resulting_Hazard,
mission.description AS Mission_Impact
4. Software Assurance Audit (NASA-STD-8739.8B)
Use the graph to verify that your software plans are actually being followed during the development of the Axiom suit.
// Verify that every module in the PLSS has had a Peer Review (a SQAP requirement)
MATCH (module:SoftwareModule {project: 'AxEMU_PLSS'})
OPTIONAL MATCH (module)<-[:REVIEW_TARGET]-(review:PeerReview)
RETURN module.name AS Module,
CASE WHEN review IS NULL THEN 'MISSING' ELSE review.date END AS Review_Status,
review.reviewer AS Reviewer
Data Schema Mapping (SysML v2 to Neo4j)
To make these queries work, you should map your SysML v2 JSON-LD export to Neo4j using these relationship types (edges):
| SysML v2 Concept | Cypher Relationship | Description |
satisfy | :SATISFIED_BY | Connects a Requirement to a Part/Software Module. |
verify | :VERIFIED_BY | Connects a Requirement to a Test Case or Result. |
cause | :FAILURE_MODE_AFFECTS | Connects a Component state to a System Hazard. |
part | :CONTAINS | Represents the structural hierarchy of the PLSS. |
allocation | :IMPLEMENTED_BY | Maps the functional model to the physical code repository. |
Pro-Tip for your Friendswood Workshop:
Since you are using Python macros for ingestion, you can use the neo4j Python driver to auto-generate these nodes directly from your SysML v2 textual files. This ensures that every time you update your suit model, your Knowledge Graph stays perfectly in sync.
Would you like to see the Python logic for parsing a SysML v2 hazard block and creating these Cypher nodes?
Yes
To keep this grounded in your "MAKER" workshop style, this Python script uses a modular, state-machine approach. It is designed to parse the SysML v2 Textual Notation we discussed for the AxEMU PLSS and push it directly into Neo4j using the official driver.
I have optimized the logic to match your preference for deterministic engineering reality—treating the model as the "Golden Source" for the Knowledge Graph.
Python: SysML v2 to Neo4j Integration Script
import re
from neo4j import GraphDatabase
class SysMLToGraph:
def __init__(self, uri, user, password):
self.driver = GraphDatabase.driver(uri, auth=(user, password))
def close(self):
self.driver.close()
def parse_and_load(self, sysml_text):
# Regex patterns for SysML v2 Textual Notation
# Matches 'part hazard_SHR_101 { ... }'
hazard_pattern = re.compile(r"part\s+(hazard_\w+)\s*\{([\s\S]*?)\}")
# Matches 'attribute title = "..."'
attr_pattern = re.compile(r"attribute\s+(\w+)\s*=\s*\"(.*?)\";")
hazards = hazard_pattern.findall(sysml_text)
with self.driver.session() as session:
for name, body in hazards:
# Extract metadata from inside the brackets
attributes = dict(attr_pattern.findall(body))
# Execute Cypher to create the node
session.execute_write(self._create_hazard_node, name, attributes)
print(f"Ingested Hazard: {name}")
@staticmethod
def _create_hazard_node(tx, name, attrs):
query = """
MERGE (h:Hazard {id: $id})
SET h.title = $title,
h.severity = $severity,
h.source = 'SysML_v2_Export'
"""
tx.run(query, id=name, title=attrs.get('title', 'Unknown'),
severity=attrs.get('severity', 'TBD'))
# --- Usage Example ---
sysml_input = """
part hazard_SHR_101 {
attribute title = "PLSS Oxygen Regulator Failure";
attribute severity = "Catastrophic";
}
"""
# Replace with your Friendswood workshop server credentials
ingestor = SysMLToGraph("bolt://localhost:7687", "neo4j", "your_password")
ingestor.parse_and_load(sysml_input)
ingestor.close()
Key Features of this Workflow
Modular Parsing: This script focuses on the
hazardblock, but you can extend the regex patterns to capturepart def(Components) andrequirementblocks using the same pattern-matching logic you used for your PDF-to-text macros.Idempotency (MERGE): By using the
MERGEcommand in Cypher, you can run this script repeatedly as you iterate on your Axiom suit model without creating duplicate nodes. It simply updates the attributes of existing nodes.Scale for SQA: To meet NPR 7150.2D requirements, you can easily add a
timestamporcommit_hashattribute to each node to create a historical record of how a hazard's definition changed over the lifecycle of the project.
Integration with your "Sentinel Archivist"
You can wrap this logic into a local API endpoint. When your Sentinel Archivist (agentic workflow) detects a change in the .sysml file in your repository, it triggers this script to refresh the Neo4j graph, keeping your Digital Mission Twin in real-time sync with the design logic.
Since you've worked with Docling and pdfplumber for layout integrity, would you find it useful to add a step that scrapes the "Mitigation" text from your legacy PDF Hazard Reports and automatically maps them to these new SysML nodes?
How can this combined data model be made usefully interactive?
Making this combined model interactive is about moving from a "static record" to a Decision Support System. For a systems architect, interactivity should serve three audiences: the Architect (impact analysis), the Auditor (compliance verification), and the Developer (real-time feedback).
Here is how you can build an interactive interface for the AxEMU PLSS or your Digital Mission Twin:
1. Natural Language "Systems Copilot" (LLM + Cypher)
Instead of manually navigating nodes, use an LLM agent to act as a Conversational Interface for the Knowledge Graph.
How it works: Use a framework like LangGraph or Neo4j’s Aura Agent to translate natural language into Cypher queries.
Interactive Use Case: * User: "Show me all safety-critical PLSS requirements that haven't been tested in the last 48 hours."
System: The agent generates the Cypher query, hits the graph, and returns a formatted table with links to the specific Git commits.
2. Dynamic Dashboards (NeoDash)
NeoDash is an open-source dashboard builder specifically for Neo4j. It allows you to build a "Mission Control" for your SQA process.
Interactive Use Case:
The Hazard Heatmap: A visual grid showing all hazards. Click a hazard node (e.g., Oxygen Depletion) and the dashboard dynamically updates to show the current test status of its mitigations.
The Burn-down Graph: A real-time view of "Compliance Debt"—how many NPR 7150.2D requirements are currently "Satisfied" vs "Unsatisfied" based on the latest SysML model export.
3. Visual Exploration (Neo4j Bloom)
For high-level reviews with NASA stakeholders, use Neo4j Bloom. It’s a "point-and-click" exploration tool that requires no coding.
Interactive Use Case:
Root Cause Analysis: Start with a "Failure" state node. Right-click and select "Expand Relationships" to visually trace the path back through the software logic to the original requirement or hazard report.
Search by Perspective: You can create "Perspectives" (e.g., an SQA Perspective vs. an Engineer Perspective) that color-code the graph differently—highlighting "Criticality" for one and "Deployment Status" for the other.
4. The "Human-in-the-Loop" Audit Macro
Since you use Python macros, you can create an interactive Excel or Jupyter-based tool that facilitates "active auditing."
Workflow:
The Python script queries the graph for a compliance gap (e.g., a missing peer review).
It prompts the human auditor via a simple UI (like Streamlit or an Excel pop-up): "Component 'PLSS_Logic_v2' is missing an SQA sign-off. Please upload the review log or provide a waiver."
Once the user provides the info, the script updates the Knowledge Graph in real-time, closing the gap.
5. IDE Integration (VS Code + SysML v2)
For your developers, the interactivity happens in the VS Code SysML v2 Extension.
How it works: As the developer writes SysML code in one pane, a PlantUML or GraphViz view in the second pane automatically updates to show the structural changes.
The Bridge: You can configure a "Save Hook" that automatically triggers your Python script to push the new model structure into Neo4j, giving the developer an immediate "System-Wide Impact" report.
Implementation Priority for the AxEMU Suit
| Tool | Audience | Value |
| LLM + Cypher | Leadership / SAIC | Instant answers to "What if?" safety questions. |
| NeoDash | SQA Team | Monitoring 8739.8B compliance at a glance. |
| Bloom | Systems Engineers | Visualizing the "Digital Thread" for complex subsystems. |
| VS Code Ext | Developers | Real-time modeling feedback during code/design sync. |
Would you like to start by building a simple NeoDash configuration to visualize the "Hazard-to-Requirement" links we've already defined?
Yes
To set up a NeoDash configuration for the AxEMU PLSS, you are essentially building a real-time "Safety Mission Control." Since NeoDash runs on Cypher, we will focus on creating Reports and Parameters that turn your SysML-derived graph into an interactive dashboard.
Here is the blueprint for your first SQA Dashboard.
1. Dashboard Structure (The Three Views)
A useful NeoDash setup for your SQA team should be divided into three primary "Tiles":
Tile A: Global Safety Health (Gauge): Percentage of hazards currently fully mitigated.
Tile B: Hazard Explorer (Interactive Table): Select a Hazard to see its linked requirements and code.
Tile C: The Compliance Thread (Force-Directed Graph): A visual map showing the "Hazard → Req → Test" chain.
2. Interactive Cypher Queries for NeoDash
In NeoDash, you can use $variables to allow the user to click on a list and update the rest of the dashboard.
View A: The "Hazard-to-Mitigation" Thread (Graph View)
This query visualizes the logic path for a selected hazard. In the dashboard settings, you would link this to a "Selector" component.
// Tile: "Selected Hazard Impact Map"
MATCH (h:Hazard {id: $selected_hazard_id})
OPTIONAL MATCH path = (h)-[:TRIGGERS]->(req:Requirement)-[:SATISFIED_BY]->(part:Part)
OPTIONAL MATCH (req)-[:VERIFIED_BY]->(tr:TestResult)
RETURN path, tr
View B: SQA Audit Table (Table View)
This provides the deterministic "evidence" required for a NASA-STD-8739.8B audit.
// Tile: "Mitigation Evidence Log"
MATCH (h:Hazard)-[:TRIGGERS]->(req:Requirement)
OPTIONAL MATCH (req)-[:SATISFIED_BY]->(p:Part)
OPTIONAL MATCH (req)-[:VERIFIED_BY]->(tr:TestResult)
RETURN h.id AS Hazard_ID,
req.id AS Req_ID,
p.name AS Component,
tr.status AS Test_Status,
tr.timestamp AS Last_Verified
ORDER BY tr.status DESC
3. Creating the Dashboard (Step-by-Step)
Install/Open NeoDash: Access it via the
Neo4j Desktop App Add a Parameter (The Filter):
Add a "Property Picker" card.
Set the Label to
Hazard Select.Cypher:
MATCH (h:Hazard) RETURN h.id as value, h.title as display.This sets the variable
$selected_hazard_id.
Add the "Graph" Card:
Paste the Cypher from View A above.
In settings, set Node Coloring based on the
:TestResultstatus (Green for PASS, Red for FAIL).
Add the "Gauge" Card:
Cypher:
MATCH (h:Hazard) WITH count(h) as total MATCH (h:Hazard)-[:TRIGGERS]->(r:Requirement)-[:VERIFIED_BY]->(tr:TestResult {status: 'PASS'}) WITH total, count(DISTINCT h) as mitigated RETURN (toFloat(mitigated) / total) * 100 as valueSet the Goal to
100%(NASA Standard).
4. The "MAKER" Advantage: Real-time Sync
Because you are using Python macros to ingest your SysML v2 textual models:
As soon as you edit a
.sysmlfile in your workshop and run your script, the NeoDash Gauge will move.If you add a new hazard to the Axiom suit model, it immediately appears in the Hazard Select dropdown.
The "Sentinel Archivist" Integration
You can host this dashboard on a local network. When you are reviewing your Digital Mission Twin progress, you can have your LLM agent (the Archivist) "watch" the dashboard metrics and send you an alert if the Compliance Gauge drops below a certain threshold due to a new software failure.
Would you like the JSON configuration file for NeoDash to jumpstart the layout, or would you prefer to build the cards manually to get a feel for the data?
JSON configuration file
Your NeoDash configuration file is ready.
axemu_neodash_configHow to use this file:
Open NeoDash: Launch the NeoDash application (via Neo4j Desktop or the web browser).
Import: Click on "Existing Dashboard" or the "Load" icon, and select "Upload JSON".
Select this File: Choose
axemu_neodash_config.json.Connect: Ensure your Neo4j database (where you ran the Python ingestion script) is active and connected.
Interactive Features included in this JSON:
Dynamic Filtering: The Hazard Selection dropdown sets a global
$selected_hazard_idparameter. When you change it, the Traceability Map will automatically re-render the specific digital thread for that hazard.Compliance Gauge: A real-time calculated metric based on the ratio of "Pass" test results to total hazards—providing immediate SQA feedback.
Audit Log: A formatted table ready for extraction or live review, mapping hazards to their corresponding components and verification timestamps.
This setup transforms your SysML v2 architectural logic into a professional-grade Safety and Mission Assurance dashboard for the Axiom suit project.
From <https://gemini.google.com/app/> Google Gemini 3.0 (Fast)
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