Assessing the Effect of Aspect Refactoring on Multi-Agent Applications
Why this work is in the frame
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Bibliographic record
Abstract
Multi Agent Systems (MAS) are increasingly gaining importance as a powerful paradigm to designing and implementing distributed applications. However, existing multi-agent applications are developed without considering the separation of non-functional concerns from the functional ones. This makes the implementation, comprehension and maintenance of multi-agent applications hard tasks. Aspect-Oriented Refactoring (AOR) is a promising technique for improving modularity and reducing complexity of existing object oriented software systems by encapsulating crosscutting concerns. The authors present, in this paper, a new dynamic approach for investigating empirically the effect of AOR on MAS applications. They focus, particularly, on the effect of AOR on agent behavior in terms of communication. The proposed approach is supported by a multi-agent profiling tool working on AgentFactory platform.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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