Enhancing domain-specific software architecture recovery
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
Performing software architecture analysis and recovery on a large software system is expensive and time consuming; when it is done at all, it is often performed within a narrow context, focused on a few areas of particular concern. However, for a long-lived system within a well understood application domain, the costs for performing detailed architecture recovery may be amortized over several generations of the system; the resulting models can also be broadened and put into context by incorporating information about the history and anticipated future evolution of both the application and its underlying domain. This paper proposes a systematic approach for organizing application domain knowledge into a unified structure called the Architectural Domain Assets Set (ADAS). The ADAS structure builds on previous research, as well as our experience in performing an architecture recovery of IBM's DB2. Our initial experiences in using ADAS suggest that it brings needed focus to the recovery process and provides assistance to domain-specific architecture recovery.
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 | 0.000 |
| 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