Feature transformation for improved software bug detection and commit classification
Bibliographic record
Abstract
Testing and debugging software to fix bugs is considered one of the most important stages of the software life cycle. Many studies have investigated ways to predict bugs in software artifacts using machine learning techniques. It is important to consider the explanatory aspects of such models for reliable prediction. In this paper, we show how feature transformation can significantly improve prediction accuracy and provide insight into the inner workings of bug prediction models. We propose a new approach for bug prediction that first extracts the features, then finds a weighted transformation of these features using a genetic algorithm that best separates bugs from non-bugs when plotted in a low-dimensional space, and finally, trains predictive models using the transformed dataset. In our experiment using the proposed feature transformation, the traditional machine learning and deep learning classifiers achieved an average improvement of 4.25% and 9.6% in recall values for bug classification over 8 software systems compared to the models built on original data. We also examined the generalizability of our concept for multiclass classification tasks such as commit classification in software systems and found modest improvements in F1-scores (sometimes up to 3%) for traditional machine learning models and 4% with deep learning models. • Feature transformation techniques applied to bug detection in software systems. • Genetic algorithm based transformation and t-SNE based clustering in low dimensions. • Improved explainability and accuracy of machine learning based bug detection models. • Applicable to deep learning models and generalizable to commit classification.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".