Application of design combinatorial theory to scenario-based software architecture analysis
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
Design combinatorial theory for test-ease generation has been used successfully in the past. It is useful in optimizing test cases as it is practically impossible to exhaustively test any software system. The same concept can be applied while doing high level architecture analysis of a software system. In software architecture analysis, the architect often analyzes different scenarios that a system may experience during its lifecycle to ensure that all or most possible scenarios arc covered in the design. Usually, the analysis is conducted manually in an ad-hoe fashion and scenarios are executed separately. However, some important use cases that involve multiple concurrent scenarios may be overlooked with this approach. Software architecture analysis is critical, especially for real time telecommunications systems. More formalism or robustness needs to be considered in the evaluation process, particularly for reliability. This paper demonstrates application of ihe design combinatorial theory based technique and tool to software architecture reliability analysis of a practical real-time software system.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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