A component‐based development process for trustworthy systems
Bibliographic record
Abstract
SUMMARY This paper introduces a new process for a rigorous component‐centered development of trustworthy systems. The rationale for our perspective of the process is two‐fold. The activities prescribed in a conventional software engineering development process are neither suitable nor sufficient for developing component‐based systems. Component‐based development methods that are currently practised do not support the explicit specification of trustworthiness properties, and are not based on rigorous principles. Hence they are not suitable for developing trustworthy systems. Trustworthiness is regarded as a composite nonfunctional property comprising the four attributes safety, security, availability, and reliability. They must be rigorously defined for components and systems composed from them. It is essential that the process enforces a direct evidence of trustworthiness in the systems that are developed following the process. Consequently, the development process, in addition to being reuse‐oriented, component‐oriented, and rigorous in all phases of the system lifecycle, should maintain the chain of evidence that the trustworthiness properties are preserved in every activity of every phase of system development. The proposed process includes several parallel interrelated tracks including component development, component assessment, component reuse, and component‐based system development and prescribes specific activities and tools for ensuring trustworthiness in all activities. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".