A Proposal of a New Class Cohesion Criterion: An Empirical Study.
Bibliographic record
Abstract
Class cohesion refers to the degree of the relatedness of the members in a class.It is considered as one of most important object-oriented software attributes.Several metrics have been proposed in the literature in order to measure class cohesion in objectoriented systems.They capture class cohesion in terms of connections among members within a class.The major existing class cohesion metrics are essentially based on instance variables usage criteria.It is only a special and a restricted way of capturing class cohesion.We believe, as stated in many papers, that class cohesion should not exclusively be based on common instance variables usage criteria.We introduce, in this paper, a new criterion, which focuses on interactions between class methods.We developed a cohesion measurement tool for Java programs and performed a case study on several systems.The obtained results demonstrate that our new class cohesion metric, based on the proposed cohesion criteria, captures several pairs of related methods, which are not captured by the existing cohesion metrics.
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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 | 0.025 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".