On the effectiveness of data balancing techniques in the context of ML-based test case prioritization
Why this work is in the frame
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Bibliographic record
Abstract
Regression testing is the cornerstone of quality assurance of software systems. However, executing regression test cases can impose significant computational and operational costs. In this context, Machine Learning-based Test Case Prioritization (ML-based TCP) techniques rank the execution of regression tests based on their probability of failures and execution time so that the faults can be detected as early as possible during the regression testing. Despite the recent progress of ML-based TCP, even the best reported ML-based TCP techniques can reach 90% or higher effectiveness in terms of Cost-cognizant Average Percentage of Faults Detected (APFDc) only in 20% of studied subjects. We argue that the imbalanced nature of used training datasets caused by the low failure rate of regression tests is one of the main reasons for this shortcoming. This work conducts an empirical study on applying 19 state-of the- art data balancing techniques for dealing with imbalanced data sets in the TCP context, based on the most comprehensive publicly available datasets. The results demonstrate that data balancing techniques can improve the effectiveness of the best-known ML-based TCP technique for most subjects, with an average of 0.06 in terms of APFDc.
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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 | 0.004 | 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 | 0.000 |
| 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