RECODE: SOFTWARE PACKAGE REFACTORING VIA COMMUNITY DETECTION IN BIPARTITE SOFTWARE NETWORKS
Bibliographic record
Abstract
It is an intrinsic property of real-world software to evolve, which is usually accompanied by the increase of software complexity and deterioration of software quality. So successful software has to be reconditioned from time to time. Though many refactoring approaches have been proposed, only a few of them are performed at the package level. In this paper, we present a novel approach to refactor the package structure of object-oriented (OO) software. It uses weighted bipartite software networks to represent classes, packages, and their dependencies; it proposes a guidance community detection algorithm (GUIDA) to obtain the optimized package structure; and it finally provides a list of classes as refactoring candidates by comparing the optimized package structure with the real package structure. Through a set of experiments we have shown that the proposed approach is able to identify a majority of classes that experts recognize as refactoring candidates, and the benefits of our approach are illustrated in comparison with other two approaches.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".