DETECTING EMERGENT BEHAVIOR IN DISTRIBUTED SYSTEMS USING SCENARIO-BASED SPECIFICATIONS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Emergent behavior is an important issue in distributed systems' design. Detecting and removing emergent behavior during the design phase will lead to huge savings in deployment costs of such systems. An effective approach for the design of distributed systems is to describe system requirements using scenarios. A scenario, commonly known as a message sequence chart or a sequence diagram, is a temporal sequence of messages sent between system components. However, scenario-based specifications are prone to subtle deficiencies with respect to analysis and validation known as incompleteness and partial description. In this research, a method for detecting emergent behavior of scenario-based specification is proposed. The method is demonstrated and verified using a mine-sweeping robot as an example. Furthermore it has been demonstrated in this paper that scenario-based specifications can be used in agile software development and that the proposed methodologies in this research can be utilized effectively in agile approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it