#3676 Knowledge Graph of NASA Software Hazard Reports

Provide an executive summary of the standard contents of a NASA Software Hazard Report
Describe how adding a full set of NASA Software Hazard Reports to a program knowledge graph would be beneficial
What about for initial drafting, reviews of drafts and updates to hazard reports?
Describe the ontology and contents of a useful program software hazard report knowledge graph
Given that software hazard reports are published as PDFs that can be converted to clean text documents, describe a step-by-step process to create this program software hazard report knowledge graph

#3676 Provide an executive summary of the standard contents of a NASA Software Hazard Report

A NASA Software Hazard Report is a critical document that systematically identifies, analyzes, and proposes resolutions for software-related hazards within a project. Its primary purpose is to ensure that software functions or malfunctions do not contribute to unacceptable risk. The report is a key component of the overall system safety program and is guided by standards such as NASA-STD-8739.8A, "NASA Software Assurance and Software Safety Standard." While the specific format may vary slightly by project, the core components of the report are standardized to ensure a thorough and consistent evaluation of software hazards.

Core Components of a NASA Software Hazard Report

A comprehensive Software Hazard Report typically includes the following key sections:

Hazard Identification: This section provides a unique identifier for the hazard and a clear, concise description of the hazardous condition. It details the system state and the conditions under which the software could pose a risk.
System and Software Description: A brief overview of the system, subsystem, or operation associated with the hazard is provided. This includes a description of the software's role and its interaction with hardware and other system components.
Hazard Causal Analysis: This is a crucial section that delves into the potential causes of the hazardous condition. It identifies specific software behaviors, failure modes, or external triggers that could lead to the hazard. This may involve techniques like Fault Tree Analysis (FTA) or Software Failure Modes and Effects Analysis (SFMEA).
Risk Assessment: The severity and likelihood of the hazard are evaluated to determine the overall risk. This assessment often uses a risk matrix to categorize the hazard's criticality (e.g., Catastrophic, Critical, Moderate, Negligible) and its probability of occurrence.
Hazard Controls and Mitigations: This section outlines the measures implemented to eliminate the hazard or reduce its associated risk to an acceptable level. These controls can be preventative (e.g., design features, coding standards) or mitigative (e.g., error detection and recovery mechanisms, warnings). The rationale for selecting specific controls is also documented.
Verification and Validation: For each hazard control, this section details the methods that will be used to verify its implementation and validate its effectiveness. This can include specific tests, analyses, inspections, or demonstrations. Traceability from the hazard to the controls and their verification is a key aspect.
Residual Risk: After all controls and mitigations are in place, any remaining risk is documented as residual risk. This section provides the rationale for accepting this residual risk, which is typically reviewed and approved by project management and safety authorities.
Status and Closure: The report tracks the status of the hazard through its lifecycle, from identification to closure. A hazard is typically closed when all associated controls have been successfully verified and the residual risk is deemed acceptable. This section includes signatures from responsible parties, indicating their concurrence with the findings and the closure of the hazard.

Describe how adding a full set of NASA Software Hazard Reports to a program knowledge graph would be beneficial

Integrating a full set of NASA Software Hazard Reports into a program knowledge graph offers significant benefits by transforming static, document-based safety information into a dynamic, queryable, and interconnected data source. This integration provides a more holistic and proactive approach to system safety and mission assurance.

Enhanced Risk Analysis and Traceability 📈

A knowledge graph creates explicit, machine-readable links between various program elements. By adding software hazard reports, you can directly connect a specific hazard to the software component that could cause it, the mitigation strategies designed to prevent it, the test cases that verify those mitigations, and the system requirements it relates to.

This creates a powerful web of traceability. For instance, you could instantly query:

"Show all hazards associated with the guidance and navigation software."
"Which test procedures verify the mitigations for critical landing phase hazards?"
"What is the ripple effect of a change in the thruster control module on overall system safety?"

This interconnected view allows for a much deeper and more efficient analysis of risks than is possible by manually cross-referencing separate documents.

System-Wide Impact Analysis 💥

When a change is proposed for a software module, a knowledge graph can quickly identify all the potential safety implications. By traversing the graph, engineers can see not just the direct hazards associated with that module, but also indirect or cascading failures that might be introduced in other parts of the system. This allows for a more comprehensive and automated impact analysis, ensuring that a seemingly minor code change doesn't inadvertently compromise mission safety.

Automated Compliance and Verification ✅

NASA operates under stringent safety standards. By mapping the contents of the hazard reports—such as hazard causes, controls, and verifications—into the knowledge graph, it becomes possible to automate compliance checks. You can run queries to ensure that every identified hazard has a corresponding mitigation, that every mitigation has been verified by a test, and that the residual risk is documented and accepted. This helps in continuously monitoring the program's adherence to safety protocols and can flag any gaps in the safety case automatically.

Improved Knowledge Retention and Onboarding 🧠

Complex aerospace programs have long lifecycles and often experience personnel changes. A knowledge graph serves as a structured, long-term repository of critical safety knowledge. New team members can quickly get up to speed by exploring the graph to understand the system's safety-critical areas, the rationale behind certain design decisions, and the history of how specific risks have been addressed. This prevents the loss of valuable "tribal knowledge" and ensures that safety lessons learned are preserved and easily accessible throughout the program's life.

In essence, adding software hazard reports to a knowledge graph moves a program from a document-centric to a data-centric approach to safety, leading to more robust, verifiable, and intelligent risk management.

What about for initial drafting, reviews of drafts and updates to hazard reports?

Of course. Integrating a knowledge graph streamlines the entire lifecycle of a hazard report, from its creation to its final updates.

Initial Drafting 📝

For initial drafting, a knowledge graph acts like an intelligent assistant. By leveraging historical data from past projects stored within the graph, it can identify and suggest potential hazards based on the software components being used. For example, if a developer is working on a new flight control module, the graph can pull up previously identified hazards related to similar modules.

It can also recommend proven mitigation strategies and controls that were successful in the past, saving time and preventing engineers from "reinventing the wheel." This helps ensure that the initial draft is more complete and builds upon the accumulated knowledge of the entire organization.

Reviews of Drafts 🧐

During the review process, the knowledge graph provides a powerful tool for validation and verification. Reviewers can run queries to automatically check for completeness and consistency.

Key benefits for reviewers include:

Automated Traceability Checks: The graph can instantly verify that every identified hazard has a corresponding cause, control, and verification method. Any broken links or missing pieces of information are immediately flagged.
Consistency Analysis: It can check if similar hazards across different subsystems are being treated with a consistent level of rigor and similar mitigation strategies.
Visualizing Connections: Reviewers can visualize the complex relationships between software, hardware, and potential failures, making it easier to spot subtle, system-level risks that might be missed in a purely document-based review.

Updates to Hazard Reports 🔄

Systems and software are not static; they evolve. When a change occurs, updating traditional documents can be a cumbersome and error-prone process. A knowledge graph makes this process much more robust.

When a software module is updated, the graph can automatically:

Identify Affected Hazards: It immediately flags all hazard reports linked to that module.
Assess the Impact of Changes: It helps engineers understand if the change resolves an existing hazard, has no impact, or inadvertently introduces a new one. The graph can trace the potential ripple effects of the change across the entire system.
Maintain Version Control: It can link different versions of a hazard report to specific software builds, creating a clear and auditable history of how safety risks have been managed throughout the project's lifecycle.

By structuring the hazard report data in a graph, the entire process becomes more dynamic, data-driven, and less susceptible to human error.

Describe the ontology and contents of a useful program software hazard report knowledge graph

A useful program software hazard report knowledge graph is structured around a central ontology that defines the key entities and their relationships. This structure transforms static report data into a powerful, interconnected model for advanced safety analysis.

The Core Ontology 🏛️

The ontology is the conceptual blueprint of the knowledge graph. It defines the "things" (nodes) and the "connections" (edges) between them. A robust ontology for software hazard reports would include:

Hazard: The central node representing a potential condition that can lead to an accident. Attributes include HazardID, description, severity, and likelihood.
Software Component: A specific piece of software, like a module, function, or library. Attributes include ComponentName, version, and functionDescription.
Cause: The specific software behavior or failure mode that could lead to the hazard (e.g., "timing violation," "incorrect output," "memory leak").
Control/Mitigation: A design feature, process, or safety mechanism implemented to prevent the cause or mitigate the hazard's effects. Attributes could include controlType (e.g., preventative, detective) and rationale.
Verification: A specific test, analysis, or inspection used to prove that a control is implemented correctly and is effective. Attributes include verificationMethod (e.g., test, inspection) and verificationStatus (e.g., passed, failed).
System Requirement: The top-level requirement that the software component and its associated safety features support.
Risk: An entity that combines the severity and likelihood of a hazard, often represented by a risk matrix score (e.g., "High," "Medium," "Low").

Relationships and Contents 🕸️

The real power comes from the relationships (edges) that connect these nodes and the rich data they contain.

Key relationships would include:

A Software Component hasPotentialCause leading to a Cause.
A Cause canTrigger a Hazard.
A Hazard isMitigatedBy a Control.
A Control isVerifiedBy a Verification.
A Verification tracesTo a System Requirement.
A Hazard hasAssociatedRisk of a certain Risk level.

The contents of the graph are the instantiated data from all the program's hazard reports. For example, the graph would contain a specific instance like: Hazard SH-042: Uncommanded Engine Shutdown, which isMitigatedBy Control C-113: Redundant Fuel Flow Sensor Check, a control that is part of Software Component EngineControlModule v2.1. This control, C-113, isVerifiedBy Test Procedure ETP-088.

This structured ontology and rich content enable you to ask complex questions that are impossible to answer with isolated documents, such as: "Show me all high-risk hazards whose mitigations have not yet passed verification testing for the landing phase software."

Given that software hazard reports are published as PDFs that can be converted to clean text documents, describe a step-by-step process to create this program software hazard report knowledge graph

Excellent question. Transitioning from static text documents to a dynamic knowledge graph involves a multi-stage process that combines data preparation, Natural Language Processing (NLP), and graph database technology.

Here is a step-by-step process to create this program software hazard report knowledge graph.

Step 1: Establish the Foundation (Ontology and Tools)

Finalize the Ontology: Before processing any text, formally define the ontology as described previously. This means deciding on the exact nodes (e.g., Hazard, SoftwareComponent, Control, Verification), their properties (e.g., a Hazard has a severity and description), and the relationships between them (e.g., isMitigatedBy, isVerifiedBy). This schema is the blueprint for your graph.
Select the Technology Stack:
- Graph Database: Choose a database to store and query the graph. Popular choices include Neo4j (which uses the Cypher query language) or Amazon Neptune (which supports multiple query languages).
- NLP Framework: Select a library for text processing, such as spaCy or a transformer-based framework like Hugging Face, which is excellent for custom entity and relationship extraction.
Aggregate Documents: Collect all the clean text versions of the software hazard reports into a centralized, accessible location.

Step 2: Information Extraction using NLP

This is the core step where you convert unstructured text into structured data (nodes and relationships).

Named Entity Recognition (NER): The goal here is to identify and label your defined entities within the text.
- Manual Annotation: Start by manually annotating a small subset of your reports (e.g., 15-20 documents). You would highlight text like "SHR-077: Uncommanded Thruster Firing" and label it as a Hazard, and "FlightControlModule v3.4" as a SoftwareComponent. This creates a training dataset.
- Train a Custom NER Model: Use your annotated dataset to train a machine learning model to automatically find and classify these entities in new, unseen documents. This model will learn the patterns and context associated with each entity type.
Relationship Extraction: After identifying the entities, you need to establish the connections between them.
- Rule-Based Approach: Start with simple rules. For example, if a sentence contains a Control entity and a Hazard entity connected by the word "mitigates," you can extract a triple: (Control) -[:IS_MITIGATED_BY]-> (Hazard).
- Advanced NLP Models: For more complex relationships, train a model that analyzes the grammatical structure and context of sentences to predict the most likely relationship between two entities.

The output of this step will be a collection of structured triples, such as: (Hazard: SHR-077, hasAssociatedRisk, Risk: High) and (Control: C-125, isVerifiedBy, Verification: V-340).

Step 3: Graph Construction and Ingestion

Data Transformation: Write scripts (e.g., in Python) to take the extracted triples from the NLP step. These scripts will parse the triples and format them into commands that your chosen graph database can understand (e.g., Cypher CREATE statements for Neo4j).
Load the Graph: Execute these scripts to populate the graph database. The script will create a unique node for each entity and then create the relationship edges between them. For instance, it will create one node for "SHR-077" and another for "C-125," then draw an isMitigatedBy edge between them.

Step 4: Validation and Enrichment

Human-in-the-Loop Validation: The NLP process won't be 100% accurate. Have subject matter experts review portions of the generated graph. They can use visualization tools to spot incorrect relationships or misclassified entities, providing feedback to improve the NLP models.
Link Inference: Use the graph's capabilities to infer new knowledge. For example, if a Hazard is linked to a SoftwareComponent that is part of a larger Subsystem, you can infer and create a new relationship showing the Hazard indirectly affects the Subsystem.

Step 5: Deployment and User Interface

Develop a Query Interface: Create an application or dashboard that allows engineers and managers to ask questions in a user-friendly way. This interface would translate a simple question like "Show me all unverified controls for critical hazards" into the appropriate database query (e.g., Cypher or SPARQL).
Implement Visualization Tools: Integrate tools like Neo4j Bloom or Gephi to allow users to visually explore the hazard data. This visual navigation is often more intuitive for understanding complex, system-level interactions than reading tables or documents.

Step 6: Automation and Maintenance

Create a Pipeline: Automate the entire process. Set up a system that watches for new or updated hazard reports. When a new PDF is added, the pipeline should automatically convert it to text, run it through the NLP models, and update the knowledge graph accordingly. This ensures the graph remains a living, up-to-date resource for the program.

Provide python application to read the PDF files, convert them to text, clean the text, then parse them for specific data relevant to the intended knowledge graph

Of course. Here is a Python application that demonstrates a complete workflow for reading PDF hazard reports, cleaning the extracted text, and parsing it for key information using regular expressions to build a knowledge graph.

This script uses a rule-based approach with regular expressions, which is a great starting point. For a more robust, production-level system, you would replace the regex parsing with a trained Named Entity Recognition (NER) model as we discussed previously.

🐍 Python Application for Parsing Hazard Reports

This application is designed to be run from your terminal. It will process all PDF files in a specified directory and output the extracted structured data for each file in JSON format.

Prerequisites

First, you'll need to install the necessary Python library, PyMuPDF, which is a high-performance PDF processing library.

pip install PyMuPDF

The Python Code

Save the following code as a Python file, for example, parse_reports.py.

import fitz  # PyMuPDF
import re
import os
import json
from typing import Dict, List, Any

def extract_text_from_pdf(pdf_path: str) -> str:
    """Extracts all text from a given PDF file."""
    try:
        doc = fitz.open(pdf_path)
        text = ""
        for page in doc:
            text += page.get_text()
        doc.close()
        return text
    except Exception as e:
        print(f"Error reading {pdf_path}: {e}")
        return ""

def clean_text(text: str) -> str:
    """Cleans the extracted text by removing extra whitespace and line breaks."""
    # Replace multiple newlines with a single one
    text = re.sub(r'\n\s*\n', '\n', text)
    # Replace multiple spaces with a single space
    text = re.sub(r' +', ' ', text)
    return text.strip()

def parse_hazard_report(text: str) -> Dict[str, Any]:
    """
    Parses the text of a hazard report to extract key entities.
    
    NOTE: This function uses regular expressions, a rule-based approach. For higher
    accuracy on varied documents, a trained NER model would be superior.
    """
    
    # Define regex patterns for our knowledge graph ontology
    # These patterns are examples and would need to be tuned to the specific document format.
    patterns = {
        'hazard_id': r'(?i)(?:Hazard ID|Hazard No\.|SHR):\s*([A-Z0-9\-]+)',
        'hazard_description': r'(?i)Hazard Description:\s*(.*?)\n',
        'risk': r'(?i)Risk Level:\s*(High|Critical|Medium|Low|Negligible)',
        'component': r'(?i)(?:Software Component|System|Module):\s*([a-zA-Z0-9_.\-]+)',
        'control': r'(?i)(?:Control ID|Mitigation No\.|CTL):\s*([A-Z0-9\-]+)',
        'verification': r'(?i)(?:Verification|Test Case|V&V ID):\s*([A-Z0-9\-]+)'
    }
    
    # Use findall to capture all occurrences of entities like controls or components
    extracted_data = {
        'hazards': [],
        'risks': list(set(re.findall(patterns['risk'], text))),
        'components': list(set(re.findall(patterns['component'], text))),
        'controls': list(set(re.findall(patterns['control'], text))),
        'verifications': list(set(re.findall(patterns['verification'], text)))
    }
    
    # For hazards, we want to group the ID and description together.
    # We use finditer to get match objects, which allows us to find associated descriptions.
    hazard_matches = re.finditer(patterns['hazard_id'], text)
    for match in hazard_matches:
        hazard_id = match.group(1)
        # Search for the description in the text immediately following the ID
        search_area = text[match.end():match.end()+500] 
        desc_match = re.search(patterns['hazard_description'], search_area)
        description = desc_match.group(1).strip() if desc_match else "Description not found"
        extracted_data['hazards'].append({'id': hazard_id, 'description': description})

    return extracted_data

def main():
    """
    Main function to orchestrate the PDF processing and parsing.
    Processes all PDFs in the 'reports' subdirectory.
    """
    # Create a directory named 'reports' and place your PDFs inside it
    reports_dir = 'reports'
    if not os.path.exists(reports_dir):
        print(f"Error: Directory '{reports_dir}' not found.")
        print("Please create it and place your PDF hazard reports inside.")
        return

    all_parsed_data = []
    
    print("--- Starting NASA Software Hazard Report Parser ---")
    
    for filename in os.listdir(reports_dir):
        if filename.lower().endswith('.pdf'):
            pdf_path = os.path.join(reports_dir, filename)
            print(f"\nProcessing file: {filename}...")
            
            # Step 1: Extract Text
            raw_text = extract_text_from_pdf(pdf_path)
            if not raw_text:
                continue
                
            # Step 2: Clean Text
            cleaned_text = clean_text(raw_text)
            
            # Step 3: Parse for specific data
            parsed_data = parse_hazard_report(cleaned_text)
            parsed_data['source_file'] = filename # Add source for traceability
            
            all_parsed_data.append(parsed_data)
            
            # Pretty-print the JSON output for this file
            print(json.dumps(parsed_data, indent=2))

    # Here you would typically load `all_parsed_data` into a graph database
    print("\n--- Processing Complete ---")
    print(f"Successfully parsed {len(all_parsed_data)} reports.")
    # For demo, we'll just show the final collected data
    # print("\nFinal aggregated data:")
    # print(json.dumps(all_parsed_data, indent=2))


if __name__ == '__main__':
    main()

How to Use the Application

Create a directory named reports in the same location where you saved your parse_reports.py script.
Place your PDF software hazard reports inside the reports directory.
Run the script from your terminal:
python parse_reports.py

Example Output

The script will process each PDF and print a JSON object containing the extracted entities. The output for a single file might look like this:

Processing file: SHR-FlightControl-2025.pdf...{
  "hazards": [
    {
      "id": "SHR-042",
      "description": "Uncommanded engine shutdown during critical landing phase."
    }
  ],
  "risks": [
    "Critical"
  ],
  "components": [
    "EngineControlModule_v2.1",
    "FuelSensorInterface"
  ],
  "controls": [
    "C-113",
    "C-114"
  ],
  "verifications": [
    "ETP-088",
    "ETP-089"
  ],
  "source_file": "SHR-FlightControl-2025.pdf"
}

This structured output is now ready to be ingested into a graph database to create the nodes and relationships of your knowledge graph.

Provide the Cypher statements to insert the data for "hazards id:SHR-042" and its associated data in to the knowledge graph

Of course. Here are the Cypher statements to insert the data for hazard SHR-042 and its associated entities into the Neo4j knowledge graph, based on the previous example's output.

The statements use MERGE to create nodes and relationships only if they don't already exist, which prevents creating duplicate data.

🧬 Cypher Insertion Statements

This single, comprehensive query creates the hazard node first, then iterates through the lists of related data to create and connect all associated nodes.

// Step 1: Create or find the primary Hazard node
MERGE (h:Hazard {id: 'SHR-042'})
SET h.description = 'Uncommanded engine shutdown during critical landing phase.'

// Use WITH to pass the created hazard to subsequent operations
WITH h

// Step 2: Create or find the SourceDocument and link it
MERGE (doc:SourceDocument {filename: 'SHR-FlightControl-2025.pdf'})
MERGE (h)-[:DOCUMENTED_IN]->(doc)

// Step 3: Create or find the Risk level and link it
WITH h // Pass the hazard again
MERGE (r:Risk {level: 'Critical'})
MERGE (h)-[:HAS_RISK]->(r)

// Step 4: Iterate over the list of components, create/find them, and link them
WITH h // Pass the hazard again
UNWIND ['EngineControlModule_v2.1', 'FuelSensorInterface'] AS componentName
MERGE (sc:SoftwareComponent {name: componentName})
MERGE (sc)-[:POTENTIAL_CAUSE_FOR]->(h)

// Step 5: Iterate over the list of controls, create/find them, and link them
WITH h // Pass the hazard again
UNWIND ['C-113', 'C-114'] AS controlId
MERGE (c:Control {id: controlId})
MERGE (h)-[:IS_MITIGATED_BY]->(c)

// Step 6: Iterate over controls and verifications to link them
// This assumes a relationship between all controls and verifications in the report.
// A more advanced parser might determine the exact C-to-V pairings.
WITH h // Pass the hazard again
MATCH (h)-[:IS_MITIGATED_BY]->(c:Control)
UNWIND ['ETP-088', 'ETP-089'] AS verificationId
MERGE (v:Verification {id: verificationId})
MERGE (c)-[:IS_VERIFIED_BY]->(v)

RETURN h, doc, r; // Return some of the created nodes as confirmation

How It Works

MERGE: This is the key command. It's an "upsert" that finds a pattern or creates it if it doesn't exist. Using it on (h:Hazard {id: 'SHR-042'}) ensures you never have two hazards with the same ID.
SET: This command sets or updates properties on a node.
WITH: This clause allows you to pass the results from one part of the query to the next.
UNWIND: This command takes a list and unwraps it into individual rows, allowing you to perform an action (like MERGE) for each item in the list.

In a real application, you would parameterize this query, passing the JSON data directly from your Python script to the Neo4j driver.

How is that specific hazard tied back to the higher levels of the hazard report graph?

That specific hazard, SHR-042, is tied back to higher levels of the program through a series of explicit relationships that connect it to the broader system architecture and program requirements. Think of it like connecting a single street address to the city, state, and country it belongs to.

System Hierarchy Roll-Up 🪜

The most direct way a hazard is tied to higher levels is through the system's physical and logical hierarchy. While the initial parsing links the hazard to a specific SoftwareComponent, that component doesn't exist in isolation.

You would add nodes for higher-level system parts and create PART_OF relationships:

The EngineControlModule_v2.1 SoftwareComponent is PART_OF the MainEngineController HardwareComponent.
The MainEngineController is PART_OF the Propulsion Subsystem.
The Propulsion subsystem is PART_OF the LaunchVehicle System.

Now, you can run a query to find all systems affected by this specific hazard, even though the hazard was initially only linked to a low-level software module. This upward chain shows how a small software issue can impact an entire system.

Traceability to Requirements ✅

Another critical link is to the program's requirements, which represent the mission's objectives and safety goals. The connection is typically made through the verification process.

The relationship chain looks like this:

Hazard SHR-042 IS_MITIGATED_BY Control C-113.
Control C-113 IS_VERIFIED_BY Verification ETP-088.
Verification ETP-088 VALIDATES System Requirement SR-5.2.1: "The propulsion system shall maintain stable thrust during landing."

This creates a complete traceability loop. You can now see not only which hardware is affected by the hazard but also which specific mission-critical requirement is put at risk if the mitigation fails. This allows program managers to assess risk not just in technical terms but in terms of mission success.

Suggest the relationship between EngineControlModule_v2.1 and Hazard SHR-042

The most appropriate relationship is that the EngineControlModule_v2.1 is a POTENTIAL_CAUSE_FOR Hazard SHR-042.

Explanation of the Relationship 🔗

In a hazard analysis knowledge graph, it's crucial to be precise. The software component itself isn't the hazard; rather, a specific failure or unexpected behavior within that software could lead to the hazardous condition.

Therefore, the relationship is defined as:

POTENTIAL_CAUSE_FOR: This indicates that a malfunction, bug, or specific state within the EngineControlModule_v2.1 could trigger the "Uncommanded engine shutdown" (SHR-042).

This is more accurate than a simple CAUSES relationship because the software may function correctly 99.999% of the time. The relationship in a hazard graph specifically captures the potential for failure, not a guaranteed outcome. This allows engineers to trace a potential system failure back to its possible software origins.

So EngineControlModule_v2.1 would likely have a set of hazards in addition to SHR-042

Yes, absolutely. It's almost certain that a critical component like EngineControlModule_v2.1 would be linked to a whole set of hazards, not just SHR-042.

A Component's Hazard Profile 📝

A single software module is rarely a single point of failure for just one specific problem. It has numerous functions and can fail in multiple ways, each potentially leading to a different hazardous condition.

For the EngineControlModule_v2.1, in addition to an uncommanded shutdown, you would likely analyze hazards such as:

SHR-043: Failure to Shut Down on Command: The engine continues to burn when it's supposed to be off, which could be catastrophic during staging or landing.
SHR-044: Incorrect Thrust Output: The module provides too much or too little fuel, causing the vehicle to deviate from its trajectory.
SHR-045: Engine Instability or Rough Combustion: The module fails to properly manage engine parameters, leading to vibrations that could damage the vehicle structure.
SHR-046: Reporting False Engine Telemetry: The module sends incorrect data to the main flight computer, causing other systems to make bad decisions.

Knowledge Graph Representation 🕸️

This is where the knowledge graph model excels. It naturally represents this one-to-many relationship. The single EngineControlModule_v2.1 node would have multiple outgoing POTENTIAL_CAUSE_FOR relationships, each pointing to a different hazard node.

This allows you to query the graph for the complete "hazard profile" of any component, giving you a comprehensive view of its total contribution to mission risk.

So for which of EngineControlModule_v2.1 SoftwareComponent, MainEngineController HardwareComponent, the Propulsion Subsystem, and the LaunchVehicle System would the hazard report be written for?

The hazard report would formally be written for the Propulsion Subsystem or the overall Launch Vehicle System.

Scope of a Hazard Report

While the root cause of the hazard is traced down to a specific component like EngineControlModule_v2.1, the hazard itself is a system-level problem. An "uncommanded engine shutdown" isn't a hazard to the software module; it's a hazard to the propulsion system and, ultimately, to the entire launch vehicle and its mission.

Think of it like a medical diagnosis. A tiny virus (SoftwareComponent) might be the root cause, but the diagnosis report is written for the patient (System) and describes the illness (Hazard) in terms of its effect on the whole body.

Therefore, the hazard report:

Is scoped at a high level (Subsystem or System) to analyze the overall operational effects and mission impact.
Contains a detailed causal analysis that traces the hazard's origin down to the lower-level hardware (MainEngineController) and software (EngineControlModule_v2.1) components.

So, all the entities you listed are critically important parts of the analysis, but the report's official scope and title would address the higher-level system where the danger actually manifests.

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