A Hybrid System for Detection of Implied Scenarios in Distributed Software Systems (S)
Bibliographic record
Abstract
Distributed software systems (DSS) are usually open-ended systems used in different domains such as robotics, energy, health, etc. Multi-agent system (MAS) are a sub-class of DSS.In DSS, maintaining consistency between the system iterations is a complex and expensive task that requires coping with requirements changes and systems upgrading.The interactions, complexity and decentralized communication between components of the DSS may emerge an unwanted behavior.An unwanted behavior, known as Emergent Behavior (EB) or Implied Scenario (IS), could lead to irreversible damages.Thus, detecting IS at an early stage of the system development is needed to decrease the cost of maintaining the system.This work focuses on verification of DSS that its requirements modeled using Message Sequence Chart (MSC).The system verification focuses on the detection of IS using two already proposed different approaches.This article presents the combination of the two approaches by improving the usability of the tool presented in the first approach and the catalogue presented in the second approach.This combination allows the detection of new implied scenarios not detected using the cited approaches separately.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".