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
This paper presents UMLDiff, an algorithm for automatically detecting structural changes between the designs of subsequent versions of object-oriented software. It takes as input two class models of a Java software system, reverse engineered from two corresponding code versions. It produces as output a change tree, i.e., a tree of structural changes, that reports the differences between the two design versions in terms of (a) additions, removals, moves, renamings of packages, classes, interfaces, fields and methods, (b) changes to their attributes, and (c) changes of the dependencies among these entities. UMLDiff produces an accurate report of the design evolution of the software system, and enables subsequent design-evolution analyses from multiple perspectives in support of various evolution activities. UMLDiff and the analyses it enables can assist software engineers in their tasks of understanding the rationale of design evolution of the software system and planning future development and maintenance activities. We evaluate UMLDiff's correctness and robustness through a real-world case stud.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it