Empirical Analysis of Data Sampling-Based Decision Forest Classifiers for Software Defect Prediction
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
The strategic significance of software testing in ensuring the success of software development projects is paramount. Comprehensive testing, conducted early and consistently across the development lifecycle, is vital for mitigating defects, especially given the constraints on time, budget, and other resources often faced by development teams. Software defect prediction (SDP) serves as a proactive approach to identifying software components that are most likely to be defective. By predicting these high-risk modules, teams can prioritize thorough testing and inspection, thereby preventing defects from escalating to later stages where resolution becomes more resource intensive. SDP models must be continuously refined to improve predictive accuracy and performance. This involves integrating clean and preprocessed datasets, leveraging advanced machine learning (ML) methods, and optimizing key metrics. Statistical-based and traditional ML approaches have been widely explored for SDP. However, statistical-based models often struggle with scalability and robustness, while conventional ML models face challenges with imbalanced datasets, limiting their prediction efficacy. In this study, innovative decision forest (DF) models were developed to address these limitations. Specifically, this study evaluates the cost-sensitive forest (CS-Forest), forest penalizing attributes (FPA), and functional trees (FT) as DF models. These models were further enhanced using homogeneous ensemble techniques, such as bagging and boosting techniques. The experimental analysis on benchmark SDP datasets demonstrates that the proposed DF models effectively handle class imbalance, accurately distinguishing between defective and non-defective modules. Compared to baseline and state-of-the-art ML and deep learning (DL) methods, the suggested DF models exhibit superior prediction performance and offer scalable solutions for SDP. Consequently, the application of DF-based models is recommended for advancing defect prediction in software engineering and similar ML domains.
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.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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