A model driven framework for N-version programming
Bibliographic record
Abstract
Complex systems-of-systems (SoS) requiring fault-tolerance and high reliability often require redundant systems. The concept of redundancy that includes components with differing failure modes is well understood in the realm of hardware design. N-version programming, although shown to produce significant gains in software reliability over single-version fault tolerant techniques, is not widely accepted or applied. This is due, in part, to N-version programming's lengthy development time and its inherent problems with version independence. Model Driven Software Development (MDSD) is a process that promises gains in software productivity and quality. While progress in MDSD has witnessed the expansion of the supporting Unified Modeling Language profile for modeling fault tolerant characteristics, and the development of specific design patterns for the production of fault tolerant software, MDSD's support in the generation and testing of fault tolerant applications is not evident or explicitly defined. This paper discusses the development of a fault tolerant MDSD framework, which enables users to design, implement and test fault tolerant applications using the N-version modeling technique. The framework closes the gap between existing modeling patterns and the practical application of fault tolerant MDSD, and supports follow-on research to address specific questions relating to the benefits of MDSD within the fault tolerance software domain.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 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".