Qualitative Analysis for the Impact of Accounting for Special Methods in Object-Oriented Class Cohesion Measurement
Bibliographic record
Abstract
Abstract — Class cohesion is a key object-oriented software quality attribute. It refers to the degree of relatedness of class attributes and methods. Several class cohesion metrics are proposed in the literature. However, the impact of considering the special methods (i.e., constructors, destructors, and access and delegation methods) in cohesion calculation is not thoroughly theoretically studied for most of the existing cohesion metrics. An incorrect determination of whether to include or exclude the special methods in cohesion measurement can lead to improper refactoring decisions according to the misleading class cohesion values that are obtained. In this paper, we qualitatively analyze the impact of including or excluding the special methods in cohesion measurement on the values that are obtained by applying 19 popular class cohesion metrics. The study is based on analyzing the definitions and formulas that are proposed for the metrics. The results show that including/excluding special methods has a considerable effect on the cohesion values that are obtained and that this effect varies from one metric to another. The study shows the importance of considering the types of methods that must be accounted for when proposing a cohesion metric. Index Terms — object-oriented design, class quality, class cohesion, cohesion metric, special methods. I.
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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 | 0.014 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".