Dynamic Maintenance and Evolution of Critical Components-Based Software Using Multi Agent Systems
Bibliographic record
Abstract
Component-based development has become a commonly used technique for building complex software systems by composing a set of existing components. In general adapting an application means stopping the application and restarting it after the adaptation. This approach is not suitable for a large classes of software systems in which continuous availability is a critical requirement, hence the need of adapting dynamically the application at runtime. This paper presents an architecture based approach for dynamic adaptation in critical components based software using multi agent system.To achieve this, we use an agent based system to perform the adaptation. The agent system is guided by an architectural description. The adaptation mechanism is implemented within the connectors using the flexibility offered by the Java script language techniques. The script language Groovy is used. The evaluation is made by comparing the execution time before and after the adaptation mechanism. The paper is structured as follows: section 2 presents related works to dynamic adaptation. Section 3 describes the proposed solution to achieve a dynamic update of components-based software applications. The implementation details and some measurements relative to our solution are given in section 4. Section 5 concludes and presents some perspectives.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".