Coping with Legacy System Migration Complexity
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".