Coping with Legacy System Migration Complexity
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
During the last three decades, a considerable amount of software has been developed based on obsolete technologies (such as using procedural languages). This type of systems has undergone severe code revisions during a long time period. As a consequence, the high level of entropy combined with imprecise documentation about the design and architecture make the maintenance more difficult, time consuming, and costly. On the other hand, these systems have important economical value; many of them are crucial to their owners (Bennett, 1995). For the high cost of lost former investment and business knowledge that embedded in those systems, in many cases, simply abandon legacy systems and re-develop new systems based on new technology is not the choice. Migrating legacy system toward new emerging technology is an appropriate solution. However, migrating legacy system towards new technology is a complex system engineering work. In this paper, we propose a novel approach to reduce the migration complexity. We apply dynamic program analysis, software visualization, knowledge recovery, and divide-and-conquer techniques to cope with the complexity issue in legacy software migration project.
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 | 0.000 | 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