A UML-Based Conversion Tool for Monitoring and Testing Multi-agent Systems
Bibliographic record
Abstract
The increasing demand for multi-agent systems (MAS) in the software industry has led to development of several agent oriented software engineering (AOSE) methodologies. The autonomous agents' interaction in a dynamic software environment can potentially lead to runtime behavioral failures such as deadlock. Therefore, the MAS environment should be tested and monitored against the unwanted emergent behaviors. The AOSE methodologies usually do not cover monitoring and testing. On the other hand model-based software development practices such as the Unified Modeling Languages (UML) are commonly used in practice and are equipped with a rich set of model based testing and monitoring tools. In this paper, we propose a conversion tool to help MAS engineers use UML-based monitoring and testing tools to test and monitor MAS design and analysis artifacts created by multi-agent software engineering (MaSE) as one of the most powerful and famous AOSE methodologies.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".