---
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.