Validating object-oriented design metrics on a commercial Java application
Bibliographic record
Abstract
Many of the object-oriented metrics that have been developed by the research community are believed to measure some aspect of complexity. As such, they can serve as leading indicators of problematic classes, for example, those classes that are most fault-prone. If faulty classes can be detected early in the development project' s life cycle, mitigating actions can be taken, such as focused inspections. Prediction models using design metrics can be used to identify faulty classes early on. In this paper, we present a cognitive theory of object-oriented metrics and an empirical study which has as objectives to formally test this theory while validating the metrics and to build a post-release fault-proneness prediction model. The cognitive mechanisms which we apply in this study to object-oriented metrics are based on contemporary models of human memory. They are: familiarity, interference, and fan effects. Our empirical study was performed with data from a commercial Java application. We found that Depth of Inheritance Tree (DIT) is a good measure of familiarity and, as predicted, has a quadratic relationship with fault-proneness. Our hypotheses were confirmed for Import Coupling to other classes, Export Coupling and Number of Children metrics. The Ancestor based Import Coupling metrics were not associated with fault-proneness after controlling for the confounding effect of DIT. The prediction model constructed had a good accuracy. Finally, we formulated a cost savings model and applied it to our predictive model. This demonstrated a 42% reduction in post-release costs if the prediction model is used to identify the classes that should be inspected.
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How this classification was reachedexpand
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.009 |
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; both teacher heads agree on what is shown here.
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".