Software Migration: A Theoretical Framework (A Grounded Theory approach on Systematic Literature Review)
Bibliographic record
Abstract
Software migration has been a research subject for a long time. Major research and industrial implementations were conducted, shaping not only the techniques available nowadays, but also a good part of Software evolution jargon. To understand systematically the literature and grasp the major concepts is challenging and time-consuming. Even more, research evolves, and it does based on the assumption that many words (such as migration) have a single well-known meaning that we all share. Since since these words meanings are rarely explicit, and their usage heterogeneous, these words end up polluted with multiple and many times opposite or incompatible meanings. In our quest to understand, share and contribute in this domain, we recognize this situation as a problem. To tackle down this problem we propose a taxonomy on the sub- ject as a theoretical framework grounded on a systematic literature review. In this study we contribute a bottom-up taxonomy that links from the object of a migration to the procedure nature migration, passing by migration drivers, objectives and approaches. We contribute a classification of all our readings, and a list of research directions discovered on the process of this study.
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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 | 0.089 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.053 | 0.042 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".