Data-mining in Support of Detecting Class Co-evolution.
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
In an evolving system maintained over a long time period, there exist many non-trivial relationships among system classes, such as class co-evolutions, which usually are not easily perceivable in the source code. However, unfortunately, the continuing evolution of large, long-lived systems leads to lost information about these hidden relationships. In this paper, we propose a method for recovering such lost knowledge by data mining method. This method relies on the UMLDiff algorithm that, given a sequence of UML class models of a system, surfaces the design-level changes over its life span, thus eliminating the need for high quality modification reports and nonintuitive software code-based metrics. We employ Apriori association rule mining algorithm to the transactional database of class modifications, which elicit previously unknown or undocumented co-evolving relations among two or more classes. The recovered knowledge facilitates the overall understanding of system evolution and the planning of future maintaining activities. We report on one real world case study evaluating our approach.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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