Studying software evolution of large object‐oriented software systems using an ETGM algorithm
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
SUMMARY Analyzing and understanding the evolution of large object‐oriented software systems is an important but difficult task in which matching algorithms play a fundamental role. An error‐tolerant graph matching (ETGM) algorithm can identify evolving classes that maintain a stable structure of relations (associations, inheritances, and aggregations) with other classes and thus likely constitute the backbone of the system. Therefore, to study the evolution of class diagrams, we first develop a novel ETGM algorithm, which improves the performance of our previous algorithm. Second, we describe the process of building an oracle to validate the results of our approach to solve the class diagram evolution problem. Third, we report for the new algorithm the impact of its parameters on the F‐measure summarizing precision (quantifying the exactness of the solution) and recall (quantifying the completeness of the solution). Finally, with tuned parameters, we carry out and report an extensive empirical evaluation of our algorithm using small (Rhino), medium (Azureus and ArgoUML), and large systems (Mozilla and Eclipse). We thus show that this novel algorithm is scalable, stable and has better time performance than its earlier version. Copyright © 2010 John Wiley & Sons, Ltd.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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