Characterizing maintainability concerns in autonomic element design
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 in recent years for its vision of developing applications with self-adaptive and self-managing behavior. Due to the inherent complexity of such applications and the nature of the built-in closed-loop feedback control, maintainability issues of autonomic systems are emerging as significant concerns in autonomic system designs. This paper identifies and categorizes types of common forms of autonomic element patterns and reveals the inherent relationships among them as well as their particular maintainability concerns. The key to maintainability of self-managing systems is their embedded control loops. Good software engineering practice calls for making the control loops as independent as possible to achieve loose coupling and separate concerns. However, typical self-managing systems solutions feature arrangements of interdependent, collaborative autonomic elements. This paper outlines selected autonomic element patterns derived from requirements goal models and attribute-based architectural styles for self-adaptive systems and then identifies their particular maintainability concerns based on the characteristics of the solutionpsilas control loops. Maintainability issues for the various autonomic element patterns are illustrated using a book store example.
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 | 0.000 | 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