Defectors: A Large Scale Python Dataset for Defect Prediction
Bibliographic record
Abstract
Defect prediction has been a major research problem in the software engineering domain for the last five decades.<br> In recent years, large deep-learning models have shifted the performance of software engineering tasks to new limits and are gaining usage in defect prediction.<br> However, these defect prediction models are often limited by the quality of their datasets, which are not large or diverse enough.<br> In this paper, we present Defectors, a large dataset for both line-level and just-in-time defect prediction.<br> Defectors consist of $\approx$ 213K source code files ($\approx$ 93K defective and $\approx$ 120K defect-free files) from 25 popular python projects from various domains and organizations.<br> These projects come from a diverse set of domains including machine learning, automation, and internet-of-things.<br> Such a scale and diversity make Defectors a suitable dataset for deep learning models, especially transformer models that require large and diverse datasets to effectively generalize defect-inducing patterns to predict future defects. Dataset Description File Name Description defectors.zip The original Dataset. Find its description in Section II of the paper. bug_inducing_commits.zip Each yaml file contains a map of bugfix commits to bug-inducing commits. filtered_bug_inducing_commits.yaml A map in structure {repo_name: {bug_inducing_commits: [list of python files in the commit]}}. This file only contains the bug-inducing commits that match the filtering criteria from Section III.D. repo_links.yaml Links to the repositories we used to construct the dataset.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.024 |
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; both teacher heads agree on what is shown here.
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".