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
Software evolution is an integral part of the software life cycle. Furthermore in the recent years the issue of keeping legacy systems operational in new platforms has become critical and one of the top priorities in IT departments worldwide. The research community and the industry have responded to these challenges by investigating and proposing techniques for analyzing, transforming, integrating, and porting software systems to new platforms, languages, and operating environments. However, measuring and ensuring that compliance of the migrant system with specific target requirements have not been formally and thoroughly addressed. We believe that issues such as the identification, measurement, and evaluation of specific re-engineering and transformation strategies and their impact on the quality of the migrant system pose major challenges in the software re-engineering community. Other related problems include the verification, validation, and testing of migrant systems, and the design of techniques for keeping various models (architecture, design, source code) during evolution, synchronized. In this working session, we plan to assess the state of the art in these areas, discuss on-going work, and identify further research issues.
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