Model-based Advancements at Lockheed Martin Space Systems Company
Bibliographic record
Abstract
Lockheed Martin Space Systems Company (LMSSC) has been using modelbased tools with autogeneration of flight code for a decade. We are aggressively driving insertion of Model-based capabilities into all engineering and production disciplines using our digital tapestry, which links all stages of manufacturing – from initial concept and design to production and qualification. Our Space Vehicle Integration Lab (SVIL) has been the driving force for the embedded software migration to model-based technologies, with insertions into nearly every stage in the software life cycle from concept and requirements definition to program simulation and qualification. For embedded software, we have embraced the MathWorks Simulink model-based development tools and developed a framework in which to control the transformation of both our products and our workforce to these techniques using a consistent approach. In many cases the LMSSC has developed our own add-ons to the COTS tools available to ensure compatibility in our processes and to fill in gaps in the COTS tool capabilities. In that time the cost to develop software using model-based tools dropped 39%. Varieties of capabilities contributed to the reduction and were described in detail along with their contribution to the savings. LMSSC is pursuing a concept called Digital Tapestry. Digital Tapestry leverages the strengths of Model-Based Development to document, elaborate, and communicate the aspects of a system for all program stakeholders in a digital fashion. With detailed SysML models, the Digital Tapestry is enabling a capability called Configure to Order. Using the Configure to Order capability, expert engineers make decisions about mission capabilities and system components which when combined with the detailed SysML models rapidly and automatically elaborates the impacts to other subsystems and system components. Once the system impacts for a given change are reported, the new systems engineering products can be rapidly communicated by a set of tools developed to generate Matlab and Simulink code fragments from the SysML models. The activity models, internal block diagrams, and other interface specification models from SysML can be automatically converted to implemented Simulink and Matlab artifacts for incorporation in the system implementation and round-tripped to SysML when needed. LMSSC has seen the use of MathWorks Simulink increase dramatically in the last 7 years. Many adopting programs made rapid progress and developed novel and productive ways to use Simulink in software development and systems analysis tasks. However, while collaboration helped, limited standardization of the Simulink development among many groups (even in the same programs) led to Simulink models that were difficult to share and re-use. Seeing an opportunity to improve future performance, LMSSC embarked on the development of the
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.103 | 0.042 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".