Bibliographic record
Abstract
Assessing software effectiveness, reliability, and quality involves a systematic approach to identifying bugs within the product. The detection of bugs during software development has spurred the development of various prediction methods to address them. Predicting bugs in concurrent software products is crucial for reducing development time and costs. This study delves into experiments conducted on a publicly available bug prediction dataset, which encompasses numerous open-source software projects. Employing the Genetic algorithm, relevant features were extracted from the datasets to mitigate overfitting risks. These features were then categorized as defective or non-defective using classification techniques such as random forest, decision tree, and artificial neural networks. Evaluation of these techniques included metrics like accuracy, precision, recall, and F-score. Results revealed that random forest outperformed other algorithms in accuracy, precision, and F-score, with average scores of 83.40%, 53.18%, and 52.04%, respectively. Additionally, the neural network demonstrated superior recall, achieving an average score of 60% among the algorithms. Consequently, this system offers valuable support to software developers, aiding them in delivering high-quality software with minimal defects to customers.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".