Predicting maintainability with object-oriented metrics -an empirical comparison
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
A large number of metrics have been proposed formeasuring properties of object-oriented software such assize, inheritance, cohesion and coupling. We have beeninvestigating which of these object-oriented metrics canbe used as significant predictors for the maintainability ofsoftware. For this purpose, we have designed andconducted an empirical study based on historical datacollected from the maintenance history of a medium-sizedobject-oriented system. Unlike most related studies,indirect coupling has also been taken into account in ourwork in order to evaluate its impact. Our study uses themaintenance history of two software systems as evidencebase for linking software quality attributes to metricssuggested for object-oriented software. Our resultsindicate that size and import direct coupling metrics aresignificant predictors for measuring maintainability ofclasses while inheritance, cohesion, and indirect/exportcoupling measures are not.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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