On the effectiveness of data balancing techniques in the context of ML-based test case prioritization
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.122 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".