Dynamic Maintenance and Evolution of Critical Components-Based Software Using Multi Agent Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it