MBD, OOT and Code Generation: A Cost-Effective Way to Speed Up HMI Certification
Bibliographic record
Abstract
This white paper explains the benefits of the Model-Based Design (MBD) approach and Object-Oriented Technology (OOT) that DO-178C provides. It also specifically focuses on the usage of Models and COTS Qualifiable tools that automate or facilitate the verification and validation of avionics applications constructed from Models in order to ensure that there is no unintended function. Software running in Aircraft cockpits has dramatically increased in complexity since DO-178B's revision in 1992. Furthermore, over the past 20 years, software development methods have made significant leaps forward and DO-178B has begun to show its age with respect to the new technology introduced to facilitate software development. This year the newly revamped DO-178C standard sets the certification process record straight by embracing modern technology. DO-178C does not only solidify its foundation by clarifying its core document but also builds the infrastructure to support modern software development techniques already commonly used in avionics development for at least a decade. Fortunately, DO-178C upgrades and clarifies DO-178B. DO-178C therefore considers four techniques of contemporary software development practices which are published as supplements to the core document: 1. Software Tool Qualification Considerations (TQC) [DO-330]. 2. Model-Based Design and Verification Supplement (MBDV) [DO-331]. 3. Object-Oriented Technology Supplement (OOT) [DO-332]. 4. Formal Methods Supplement (FM) [DO-333]. Organizations can see gains not only in the reduction of the development cycle but also in the overall improvement of the DO-178C certification process; including reduction of schedule and costs, and improvements in the quality and reliability. In the old school of thought, the methodology relies on textual specifications and physical prototypes. That is why the informal Text-Based Design approach is tightly associated with the waterfall methodology where all the textual requirements are manually coded, inspected, and tested on a real embedded system. In this method, changes in any part of the waterfall chain are very costly and time-consuming, leaving almost no room to iterate on the design. By contrast, in the Model-Based Design approach, the specifications are self-contained in the Human Machine Interface (HMI) Model. The HMI requirements are defined in an unambiguous way and often captured in a formal definition language. Model-Based Design offers a collaborative approach to avionics development and allows engineers to inexpensively experiment with various concepts by deferring hardware integration until much later in the development process. Correcting problems in the early modeling phase is undeniably the strongest argument in favor of the Model-Based Design approach for developing certifiable or non-certifiable avionics applications. This white paper is based on "MBD & Code Generation: A Cost-Effective Way to Speed up HMI Certification," by Luc Marcil, Presagis, Montréal, Québec (Canada) which was presented at the 30th Digital Avionics Systems Conference in October 18th, 2011. © 2011 IEEE.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.018 |
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".