RELIABILITY MODEL FOR COMPONENT-BASED SYSTEMS IN COSMIC (A CASE STUDY)
Bibliographic record
Abstract
Software component technology has a substantial impact on modern IT evolution. The benefits of this technology, such as reusability, complexity management, time and effort reduction, and increased productivity, have been key drivers of its adoption by industry. One of the main issues in building component-based systems is the reliability of the composed functionality of the assembled components. This paper proposes a reliability assessment model based on the architectural configuration of a component-based system and the reliability of the individual components, which is usage- or testing-independent. The goal of this research is to improve the reliability assessment process for large software component-based systems over time, and to compare alternative component-based system design solutions prior to implementation. The novelty of the proposed reliability assessment model lies in the evaluation of the component reliability from its behavior specifications, and of the system reliability from its topology; the reliability assessment is performed in the context of the implementation-independent ISO/IEC 19761:2003 International Standard on the COSMIC method chosen to provide the component's behavior specifications. In essence, each component of the system is modeled by a discrete time Markov chain behavior based on its behavior specifications with extended-state machines. Then, a probabilistic analysis by means of Markov chains is performed to analyze any uncertainty in the component's behavior. Our hypothesis states that the less uncertainty there is in the component's behavior, the greater the reliability of the component. The system reliability assessment is derived from a typical component-based system architecture with composite reliability structures, which may include the composition of the serial reliability structures, the parallel reliability structures and the p-out-of-n reliability structures. The approach of assessing component-based system reliability in the COSMIC context is illustrated with the railroad crossing case study.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".