Context and Risk Overview:

In flight software systems, data inputs (e.g., software data loads, data configuration loads, I-loads, and configuration files) act as critical parameters for proper system operation. Data-driven architectures rely heavily on these inputs for decision-making, state transitions, and mission execution. Data testing is as important as the software functionality since corrupted, incomplete, or incorrect data can mislead software, leading to system failures, mission-critical anomalies, or safety hazards.

When less than 100% of data inputs are tested, the system is exposed to significant risks, including:

1. Software misbehavior due to unverified data edge cases.
2. Incomplete validation of data loading processes (e.g., I-loads and configuration files).
3. Data corruptions overlooked during testing.
4. Lack of robustness against missing, erroneous, or unexpected inputs.

The complexity of modern flight software data-driven systems necessitates comprehensive verification approaches. Missed verification efforts in these areas lead directly to system downtime, non-compliance with industry standards, in-field failures, and ultimately, mission losses—all of which are unacceptable for flight systems.

Key Risks in Testing Less Than 100% of Flight Software Data Inputs

1. Incomplete Data Load Testing

• Key Issues:
  • Inadequate testing of configuration inputs (I-loads) and data files.
  • Missing data types or invalid fields in data configurations (e.g., numeric ranges, enumerations).
  • Errors in file parsing or loading mechanisms can go undetected.
• Risks:
  1. Corrupted I-Loads: Faulty configuration inputs may cause system misconfigurations.
  2. Operational Instability: Incorrect system configuration can lead to degraded or unintended performance.
  3. Mission Failure: Anomalies may arise mid-mission due to invalid or untested data inputs.

2. Missing Negative or Edge Case Testing

• Key Issues:
  • Untested edge conditions, such as boundary values or invalid data inputs.
  • Missing input combinations that may cause software crashes.
  • Lack of robustness testing for unexpected input formats/patterns.
• Risks:
  1. Unhandled Anomalies: Lack of robustness validation leads to failures under stress.
  2. Mission Interruptions: Errors in unforeseen circumstances, such as invalid sensor readings or telemetry data.

3. Missing Verification of Data-Driven Architectures

• Key Issues:
  • Data-heavy systems like flight satellites or UAVs rely on in-flight telemetry and preloaded configurations (e.g., lookup tables, command definitions).
  • Verification of "data logic" (derived from configuration-driven software) often overlooked.
• Risks:
  1. Faulty Decisions: Inaccuracies lead to incorrect system responses and actions.
  2. Incomplete Functional Validation: The integrity between data and associated logic may be compromised.

4. Lack of Test Automation for Large Data Sets

• Key Issues:
  • Manual efforts struggle with the scale and complexity of modern data architectures.
  • Regression testing becomes unmanageable without automation.
• Risks:
  1. Reduced Test Coverage: Critical inputs might not get tested due to time or resource constraints.
  2. Incorrect Data Mapping: Unverified mappings in data flows could lead to cascading failures across subsystems.

5. Vulnerability to Missing Input Data or Corruption

• Key Issues:
  • Missing verification for partial or incomplete data loads.
  • Undetected issues with error-handling and recovery mechanisms for corrupted or missing files.
• Risks:
  1. Crash or Failure: Unhandled missing data scenarios lead to ungraceful failure instead of fallback mechanisms.
  2. Risk of Inconsistent Behavior: Data corruption may result in abnormal or unexpected software behavior.

Root Causes of Incomplete Testing

1. Insufficient Test Coverage Definition:
   • Poorly defined test plans lead to untested input scenarios (e.g., valid/invalid configurations, boundary conditions).
2. Inadequate Resource Allocation:
   • Limited tools, bandwidth, or teams may prioritize functional testing over comprehensive data validation.
3. Overreliance on Assumptions:
   • Data correctness may be assumed if format validation appears correct, without further functional testing.
4. Time and Schedule Pressure:
   • Fast deadlines often lead to skipped edge case or robustness testing for data-driven features.
5. Missing Automation Strategies:
   • Manual testing impedes large-scale data validation, making it impractical for dynamic configurations and real-world scenarios.
6. Poor Test Plan for Data-Driven Architectures:
   • The complexities of verifying the end-to-end functionality of data-driven systems, logical dependencies, and dynamic configurations may go unaccounted for in the test plan.

Mitigation Strategies

Mitigating the risk of incomplete testing for flight software data inputs and verification of data-driven architectures involves addressing test coverage, automation, robustness, and traceability.

1. Full Data Verification and Load Testing

• Develop a Data Input Test Plan for 100% coverage of data-driven components:
  • Verify data loading/transfers, including I-loads, configuration files, initialization parameters, telemetry, and temporary data.
  • Test for data corruption scenarios (e.g., truncated, incomplete, or missing fields/files).
• Validate data access paths and mapping correctness between data inputs and operational subsystems.
• Define boundary and robustness test cases (e.g., large files, corrupted input files, unexpected characters, etc.).

2. Automate Comprehensive Data Testing

• Use test automation tools to manage large and complex data scenarios:
  • Employ tools like JUnit, Python unittest, Robot Framework, or LDRA Testbed for dynamic and automated testing.
• Automate end-to-end testing:
  • From data ingestion (preloading or runtime telemetry collection) to action execution in the operational system.
  • Test automation tools to stress test large-scale input data or overlapping inputs.

3. Define a Comprehensive Data and Command Coverage Matrix

• Create a Data Coverage Matrix:
  • List all data inputs, including software command paths, I-load files, telemetry parameters, and internal preloaded configurations.
  • Map each input to its associated functionality and expected outcomes.
  • Include scenarios for both nominal operations (normal conditions) and non-nominal behavior (failure or edge cases).
• Track and ensure coverage of every input during unit, integration, system, and mission-level testing.

4. Boundary, Negative, and Edge Case Testing

• Define detailed boundary-value and equivalence class tests for each input type:
  • Test edge conditions such as maximum/minimum values, missing fields, or incorrect syntax/parameters.
• Test invalid, malformed, and borderline test cases:
  • Examples: Out-of-range sensor telemetry data, absence of expected initialization files, or malformed I-loads.
• Implement fuzz testing to simulate unstructured or semi-random data inputs in real-time operational environments.

5. Robust Validation of Data Logic for Data-Driven Components

• For data-driven systems:
  • Test dynamic decision-making logic derived from configuration files and runtime telemetry (e.g., lookup tables, threshold definitions, safety constraints).
  • Perform dependency testing to verify that changes in incoming data affect outputs/logic as expected.
• Simulate different operational modes to validate system performance under varying data configurations (e.g., nominal, degraded/safe modes).

6. Introduce Error Injection for Resilience Testing

• Inject artificial faults (e.g., incomplete files, garbage data) during the testing process to verify:
  • Error-checking mechanisms (e.g., checksum validation, error logs, etc.).
  • Recovery procedures for missing or corrupted inputs.

7. Data Integrity Verification

• Incorporate integrity checks throughout the data pipeline:
  • Use cryptographic or checksum validations during hardware-in-the-loop (HIL) or software simulations.
• Perform real-time telemetry validation during simulations and ground tests for data corruption scenarios.

8. Traceability and Formal Verification

• Leverage Requirements Traceability Matrices (RTM) to:
  • Map requirements to data-driven inputs to ensure all data flows and relationships are tested.
• Use formal methods (e.g., model checking, state-based verification) for critical data-driven architectures to identify unverified edge cases.

9. Continuous Integration (CI) Pipelines for Data Validation

• Introduce CI/CD pipelines for automated regression testing of data inputs:
  • Automatically validate all data scripts, files, and configurations after system updates.
  • Run multiple configurations and parametric variations using data-driven tests during builds.

Monitoring and Controls

1. Coverage Tracking:
   • Use tools (e.g., LDRA, gcov, Parasoft, or Jira) to monitor data input test coverage and generate reports on untested files or scenarios.
2. Defect Metrics:
   • Track failure rates linked to data inputs. Reassess areas with frequent issues (e.g., parsing routines, untested configurations, etc.).
3. Regression Testing:
   • Verify data inputs after every software or firmware update.
4. Checklist for Missing Scenarios:
   • Use readiness review checklists for System Readiness Review (SRR) and Operational Readiness Reviews (ORR) to ensure all data inputs are covered.

Consequences of Incomplete Data Input Testing

1. Mission Loss or Delays:
   • Failure of poorly tested data-driven behavior during flight operations may result in mission interruptions, total failures, or costly delays.
2. Non-Compliance:
   • Certification authorities (e.g., DO-178C, NASA STD-8739.8) emphasize the verification of data integrity. Missing verifications may lead to rejection of flight software for deployment.
3. Increased Costs:
   • Debugging data-related errors late in a project lifecycle increases costs and resource demands.
4. Safety Violations:
   • Incomplete configurations or untested telemetry errors may compromise safety in crewed systems.

Conclusion

Testing flight software data inputs comprehensively—including software loads, configuration files, and I-loads—is essential for maintaining mission reliability and safety. Ensuring 100% data coverage, automating test validation, and tracing all data-driven logic to requirements are necessary to mitigate risks. Adopting structured verification approaches with robust error testing will reduce defects, improve compliance, and ensure the success of mission-critical systems.

3. Resources

3.1 References