Self-Repair through Reconfiguration: A Requirements Engineering Approach
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
High variability software systems can deliver their functionalities in multiple ways by reconfiguring their components. High variability has become important because of current trends towards software systems that come in product families, offer high levels of personalization, and fit well within a service-oriented architecture. The purpose of our research is to propose a framework that exploits such variability to allow a software system to self-repair in cases of failure. We propose an autonomic architecture that consists of monitoring, diagnosis, reconfiguration and execution components. This architecture uses requirements models as a basis for monitoring, diagnosis, and reconfiguration. We illustrate our proposal with a medium-sized publicly available case study (an automated teller machine (ATM) simulation), and evaluate its performance through a series of experiments. Our experimental results demonstrate that it is feasible to scale our approach to software systems with medium-size requirements.
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 | 0.000 | 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 | 0.001 |
| 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