| --- |
| license: cc-by-4.0 |
| task_categories: |
| - tabular-classification |
| language: |
| - en |
| tags: |
| - software-engineering |
| - defect-prediction |
| - just-in-time |
| - szz |
| - git |
| - mining-software-repositories |
| size_categories: |
| - 100K<n<1M |
| pretty_name: git-jit-defects |
| --- |
| |
| # git-jit-defects |
|
|
| **360,463 commits from 48 open-source repositories (C/C++, Rust, Go, Python, TypeScript/JavaScript), each labelled |
| bug-inducing or not by SZZ, with 15 features derivable from local `git` alone.** Snapshot: April 2026. |
|
|
| Built for just-in-time (JIT) defect prediction that works on *unseen* repositories without any API access: |
| every feature here can be computed from a local clone at `pre-push` time, and the labels came from `git blame` only. |
| No GitHub/GitLab API, no issue tracker, no rate limits. The full pipeline (`build.py`) is included and reproducible. |
|
|
| ## What is in the box |
|
|
| | file | content | |
| |---|---| |
| | `commits.parquet` | one row per labelled commit: 18 columns (below), zstd, ~21 MB | |
| | `build.py` | the labelling + feature pipeline (bare clone → SZZ → parquet) | |
| | `benchmark.py` | baseline: LA rule vs LightGBM, 5-fold repo-level cross-validation | |
| | `repos.txt` | the repositories scanned (72 requested; 48 met the ≥2000-commit / clone criteria) | |
|
|
| ### Columns |
|
|
| | column | type | meaning | |
| |---|---|---| |
| | `repo` | str | `owner/name` on GitHub | |
| | `sha` | str | full commit hash | |
| | `t` | int | author timestamp (unix seconds) | |
| | `author` | str | sha1 of author email (pseudonymous, stable within and across repos) | |
| | `subj` | str | commit subject line | |
| | `nfiles` | int | files touched | |
| | `la` / `ld` | int | lines added / deleted | |
| | `hunks` | int | diff hunks | |
| | `ndirs` / `nexts` | int | distinct top-level directories / file extensions touched | |
| | `test_ratio` | float | share of touched files that are test files | |
| | `a_prior_n` | int | author's prior commits in this repo | |
| | `a_prior_rate` | float | author's prior bug-inducing rate in this repo (Laplace-smoothed, past only) | |
| | `a_days` | float | days since the author's first commit in this repo | |
| | `msg_len` | int | subject length (chars) | |
| | `msg_ref` | int | 1 if subject references an issue/PR (`#123`) | |
| | `y` | int | **label**: 1 = bug-inducing (SZZ), 0 = clean | |
|
|
| `benchmark.py` derives the remaining model inputs on the fly (log1p of counts, hour/day-of-week from `t`, and |
| `repo_rate` = the repo's own expanding past bug rate), so every input is legal to compute at inference time from |
| local git history. |
|
|
| The diff text and added lines are **not** included (licensing, size). `build.py` regenerates them from the public repos. |
|
|
| ## How labels were made (read this before trusting them) |
|
|
| Plain SZZ on `git blame -w`, with a few filters, no refactoring-aware step: |
|
|
| 1. **Fix commits** = subject matches |
| `\b(fix(es|ed)?|bug|bugfix|hotfix|regression|crash|segfault|leak|race|deadlock|overflow)\b` |
| and does *not* match a noise list (typo, lint, format, style, docs, ci, build, tests, chore, revert, merge, bump, deps, …); |
| ≤10 files, 1–200 removed lines; test files excluded. |
| 2. For each fix, the removed lines are blamed (`-w`); the blamed commits are **bug-inducing candidates**. |
| 3. A commit gets `y = 1` if the earliest fix blaming it lands within **365 days** of the commit. |
| 4. Only commits inside the *labelable window* are kept: after the oldest scanned fix and at least **180 days** before |
| the newest commit (so every commit had a fair chance of being fixed). |
| 5. Repos with fewer than 2000 commits were dropped. |
|
|
| Overall inducing rate is **11.7%**, per-repo from 1% (ValveSoftware/Proton) to 35% (dragonflydb/dragonfly). |
|
|
| **Known limitations.** SZZ labels are noisy; manual studies put precision of basic SZZ around 50–70% |
| (Herbold et al., [arXiv:1911.08938](https://arxiv.org/abs/1911.08938)). Fix detection is regex-based on the subject |
| line only. There is no refactoring or cosmetic-change filter. Big-bang / squash-merge commits are not treated |
| specially. No manual validation sample was taken. Treat this as a *large, cheap, multi-language* JIT dataset, not a |
| high-confidence one; for a curated C/C++ benchmark see ReDef ([arXiv:2509.09192](https://arxiv.org/abs/2509.09192)). |
|
|
| ## Baseline results (unseen repositories) |
|
|
| 5-fold cross-validation **by repository** (a repo is either entirely train or entirely test), early stopping on a |
| temporal split inside the training repos. Metric: ROC-AUC per test repo. |
|
|
| | method | pooled AUC | median repo AUC | repos beating LA rule by ≥0.02 | |
| |---|---|---|---| |
| | LA rule (rank by lines added) | 0.778 | 0.770 | – | |
| | Logistic regression, size only | 0.777 | 0.772 | 0% | |
| | **LightGBM, 15 git features** (`benchmark.py`) | **0.834** | **0.813** | **83%** | |
| | Hashed TF-IDF on diff text, LR | 0.745 | 0.737 | 8% | |
| | LightGBM, git features + TF-IDF | 0.830 | 0.811 | 85% | |
| | LightGBM, git features + UniXcoder-base embedding of the diff | 0.837 | 0.813 | 88% | |
|
|
| Take-aways we found while building it: |
|
|
| - Nearly all the gain over the LA rule comes from the interaction of change size with **author / repo history** |
| (`a_prior_rate`, `repo_rate`); ablating history drops the median to 0.795. |
| - Code semantics (TF-IDF, or a 768-d pre-trained code-change embedding) add **nothing** on unseen repos, consistent |
| with Zeng et al. 2021 and ReDef 2025. |
| - Pooled calibration looks perfect (ECE 0.005) but per-repo it is not: median ECE 0.024, p90 0.073, max 0.109; the |
| model over-predicts in low-rate repos. Calibrate per repo before using probabilities. |
|
|
| ## Reproduce / extend |
|
|
| ```bash |
| pip install polars lightgbm scikit-learn |
| python benchmark.py commits.parquet # ~5 min on a 12-core CPU |
| python build.py repos.txt 6 # rebuild from scratch (bare clones, ~1 GB disk peak per worker) |
| ``` |
|
|
| `build.py` only needs `git`; add any public repository to `repos.txt` to extend the dataset. |
|
|
| ## Citation |
|
|
| ``` |
| @misc{gitjitdefects2026, |
| title = {git-jit-defects: SZZ-labelled commits with git-only features from 48 repositories}, |
| author = {Kus, Mustafa Abdullah}, |
| year = {2026}, |
| url = {https://huggingface.co/datasets/amazeng-co/git-jit-defects} |
| } |
| ``` |
|
|
| Commit metadata belongs to the respective repositories and their authors; this derived dataset (features, labels, |
| scripts) is released under CC BY 4.0. |
|
|
