Quality Criteria and an Analysis Framework for Self-Healing Systems
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
Autonomic computing has become more prevalent and hence its evaluation is becoming more important. This paper addresses the issue of evaluating the software architecture of self-healing applications with respect to the changes and adaptation over long periods of time. To facilitate this evaluation, we developed an analysis and reasoning framework for the architecture of self-healing systems. The framework is based on attribute-based architectural styles (ABASs) and is tailored to selected quality attributes. When an autonomic system evolves, our framework can be used to re-analyze the system and verify certain quality attributes. The explicitly available relationship between architecture and quality attributes not only helps in documenting the current architecture design, but also allows developers to reuse the architectural analysis during long-term evolution when the original system designers are long gone. Hence, the proposed framework can facilitate both design and maintenance of self-healing systems. As a first step in the analysis, we identify key quality attributes for self-healing systems. We have also defined new autonomic specific quality attributes for self-healing systems. Further, we have customized the ISO 9126 quality model to the quality requirements of self-healing systems, considering both traditional attributes as well as newly defined autonomic-specific attributes.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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