SEARCH-BASED TESTING OF MULTI-AGENT MANUFACTURING SYSTEMS FOR DEADLOCKS BASED ON MODELS
Bibliographic record
Abstract
Multi-Agent Systems (MAS) have been extensively used in the automation of manufacturing systems. However, similar to other distributed systems, autonomous agents' interaction in the Automated Manufacturing Systems (AMS) can potentially lead to runtime behavioral failures including deadlocks. Deadlocks can cause major financial consequences by negatively affecting the production cost and time. Although the deadlock monitoring techniques can prevent the harmful effects of deadlocks at runtime, but the testing techniques are able to detect design faults during the system design and development stages that can potentially lead to deadlock at runtime. In this paper, we propose a search based testing technique for deadlock detection in multi-agent manufacturing system based on the MAS design models. MAS design artifacts, constructed using Multi-agent Software Engineering (MaSE) methodology, are used for extracting test requirements for deadlock detection. As the case study, the proposed technique is applied to a multi-agent manufacturing system for verifying its effectiveness. A MAS simulator has been developed to simulate multi-agent manufacturing system behavior under test and the proposed testing technique has been implemented in a test requirement generator tool which creates test requirements based on the given design models.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".