A Rich High-Order Mutation Testing Dataset for Software Fortification
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
High-order mutation (HOM) testing is a rigorous technique for evaluating the effectiveness of test suites by introducing mutations with multiple concurrent faults into the source code. In this study, we present the development and analysis of a comprehensive dataset tailored for HOM testing purposes. The dataset comprises 2,839,792 instances categorized into Survived and Killed classes, representing instances correctly identified as surviving and not surviving the mutation testing process, respectively. We employ four prominent machine learning algorithms—Logistic Regression, Random Forest Classifier, LightGBM, and XGBoost—to classify instances within these categories. Experimental results demonstrate varying levels of accuracy, precision, recall, and F1-score across the algorithms, with LightGBM and XGBoost exhibiting superior performance. These findings underscore the importance of high-quality datasets in facilitating effective HOM testing and provide valuable insights into the capabilities of machine learning algorithms in this context.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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