A Technique and a Tool to Detect Emergent Behavior of Distributed Systems Using Scenario-Based Specifications
Bibliographic record
Abstract
Distributed systems are employed in countless applications such as information systems, robotics, etc. Lack of central control makes the design of such systems a challenging task because of possible unwanted behavior at runtime, commonly known as emergent behavior. Developing a methodology to detect emergent behavior in the pre-implementation stages of the software development life-cycle of distributed systems can potentially lead to huge savings in time and cost. Moreover, due to the typical large size of the modern distributed systems, automating the detection methodology is considered greatly beneficial. An effective and efficient approach for the design of distributed systems is to describe system requirements using scenarios. A scenario, commonly known as a message sequence chart (MSC), is a temporal sequence of messages sent between system components. However, scenario-based specifications may contain subtle deficiencies with respect to analysis and validation known as incompleteness and partial description. In this research, a tool to automatically detect emergent behavior of scenario-based specification of distributed systems is developed and demonstrated using a robotics example.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".