Revisiting Class Cohesion: An empirical investigation on several systems.
Bibliographic record
Abstract
Class cohesion is considered as one of most important object-oriented software attributes. Cohesion refers to the degree of relatedness between members in a class. High cohesion is a desirable property of classes. Several metrics have been proposed in literature in order to measure class cohesion in object-oriented systems. They capture class cohesion in terms of connections between members within a class. Most of these metrics have been experimented and widely discussed. They do not take into account some characteristics of classes as stated in several papers. We present, in this paper, an extention of the cohesion metric we proposed in a previous work. We introduce a new cohesion criterion based on common objects parameters. Our main goal in this work was: (1) to demonstrate, by analyzing many real systems that the introduced criterion is statistically significant and, (2) to validate our approach for class cohesion assessment by exploring empirically the relationship that may exist between our new cohesion metric and coupling. We developed a cohesion measurement tool for Java programs and performed an empirical study on several systems. The selected test systems vary in size and domain. The obtained results demonstrate that: (1) the new class cohesion metric captures several additional pairs of related methods and (2) there exists a significant correlation between the new cohesion metric and coupling.
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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 | 0.011 | 0.118 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".