A verification-driven framework for iterative design of controllers
Bibliographic record
Abstract
Abstract Controllers often are large and complex reactive software systems and thus they typically cannot be developed as monolithic products. Instead, they are usually comprised of multiple components that interact to provide the desired functionality. Components themselves can be complex and in turn be decomposed into multiple sub-components. Designing such systems is complicated and must follow systematic approaches, based on recursive decomposition strategies that yield a modular structure. This paper proposes FIDDle–a comprehensive verification-driven framework which provides support for designers during development. FIDDle supports hierarchical decomposition of components into sub-components through formal specification in terms of pre- and post-conditions as well as independent development, reuse and verification of sub-components. The framework allows the development of an initial, partially specified design of the controller, in which certain components, yet to be defined, are precisely identified. These components can be associated with pre- and post-conditions, i.e., a contract, that can be distributed to third-party developers. The framework ensures that if the components are compliant with their contracts, they can be safely integrated into the initial partial design without additional rework. As a result, FIDDle supports an iterative design process and guarantees correctness of the system at any step of development. We evaluated the effectiveness of FIDDle in supporting an iterative and incremental development of components using the K9 Mars Rover example developed at NASA Ames. This can be considered as an initial, yet substantive, validation of the approach in a realistic setting. We also assessed the scalability of FIDDle by comparing its efficiency with the classical model checkers implemented within the LTSA toolset. Results show that FIDDle scales as well as classical model checking as the number of the states of the components under development and their environments grow.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".