Evaluating Stratification Alternatives to Improve Software Defect Prediction
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
Numerous studies have applied machine learning to the software defect prediction problem, i.e. predicting which modules will experience a failure during operation based on software metrics. However, skewness in defect-prediction datasets can mean that the resulting classifiers often predict the faulty (minority) class less accurately. This problem is well known in machine learning, and is often referred to as “learning from imbalanced datasets.” One common approach for mitigating skewness is to use stratification to homogenize class distributions; however, it is unclear what stratification techniques are most effective, both generally and specifically in software defect prediction. In this article, we investigate two major stratification alternatives (under-, and over-sampling) for software defect prediction using Analysis of Variance. Our analysis covers several modern software defect prediction datasets using a factorial design. We find that the main effect of under-sampling is significant at α = 0.05, as is the interaction between under- and over-sampling. However, the main effect of over-sampling is not significant.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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