On Designing Self-Adaptive Software 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
Self-adaptive systems modify themselves at run-time in order to control the satisfaction of their requirements under changing environmental conditions. Over the past century, feedback-loops have been used as important models for controlling dynamic behavior of mechanical, electrical, fluid and chemical systems in the corresponding fields of engineering. More recently, they also have been adopted for engineering self-adaptive software systems. However, obtaining sound and explicit mappings consistently between adaptive software architectures and feedback loop elements is still an open challenge. This paper, recalling a reference model proposed previously with that goal, discuss key aspects on the design of adaptive software where feedback loop elements are explicitly defined as first-class components in its software architecture. It complements this discussion with an illustration of the process to use this reference model by applying it to a plausible adaptive software example. This paper aims at providing a reference starting point to support software engineers in the process of designing self-adaptive software systems.
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 | 0.001 |
| 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 | 0.001 | 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