Quality Criteria and an Analysis Framework for Self-Healing Systems
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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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 | 0.017 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".