Bibliographic record
Abstract
The literature describes several approaches to identify the artefacts of programs that evolve together to reveal the (hidden) dependencies among these artefacts and to infer and describe their evolution trends. We propose the use of biological methods to group artefacts, to detect co-evolution among them, and to construct their phylogenic trees to express their evolution trends. First, we introduced the novel concepts of macro co-changes (MCCs), i.e., of artefacts that co-change within a large time interval and of dephase macro co-changes (DMCCs), i.e., macro co-changes that always happen with the same shifts in time. We developped an approach, Macocha, to identify these new patterns of artefacts co-evolution in large programs. Now, we are analysing the evolution of classes playing roles in design patterns and — or antipatterns. In parallel to previous work, we are detecting what classes are in macro co-change or in dephase macro co-change with the design motifs. Results try to show that classes playing roles in design motifs have specifics evolution trends. Finally, we are implementing an approach, Profilo, to achieve the analysis of the evolution of artefacts and versions of large object-oriented programs. Profilo creates a phylogenic tree of different versions of program that describes versions evolution and the relation among versions and programs. We will, also, evaluate the usefulness of our tools using lab and field studies.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".