Model Based Engineering for Advanced Integrated Modular Avionics - Focus and Challenges
Bibliographic record
Abstract
Advanced Integrated Modular Avionics (A-IMA) will drive new focus and challenges for Model Based Engineering (MBE). First, there is the need to bridge MBE to legacy system elements that were developed without MBE along with the need to handle hybrid Open System Architecture / Integrated Modular Avionics (OSA/IMA) based architectures. Second, there is the need for MBE to be reusable and interoperable across product development cycles as technology insertions occur. Third, there is the need for integration of MBE into synthesizable descriptions that can also be effectively validated for mixed general purpose, safety, and secure computing and networking environments. Fourth is the need for effective application of MBE in hybrid waterfall and agile development environments where target infrastructure is scalable in capability and cost. Fifth is the need for MBE to support partitioned roles across companies, government, and universities where one entity does requirements, one does architecture, one develops components, one provides formal test, and another provides system sustainment. There are a number of industry and university efforts underway to address these focus items and challenges spread across these adjacent MBE complex system domains. This paper is focused on the current state of each of these areas relative to use in A-IMA systems based on industry initiatives and academic research. It uses the driverless car for comparison as an emerging "Advanced Integrated Modular Architecture" and identifies its parallel approaches to address these focused items and challenges. This work is being built on the authors' work exploring dual use technologies being developed for the driverless car domain that will lead to a market of 10 Million autonomous cars operating in 2020. Previous papers have addressed identification of potential advanced automotive dual use transformational hardware and software technologies including many core processing, advanced software autonomy and data fusion components, unified mixed criticality networking, and integrated cyber security for A-IMA. A testbed has also been recently proposed as a mechanism to evaluate these dual use technologies in an A-IMA context. This paper extends the dual use view to include understanding of the best-of-breed avionics MBE environment and how it can be complementary to leveraging a testbed environment in addressing affordable, scalable, and open solutions.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".