README.md

metadata

license: cc-by-nc-4.0
task_categories:
  - text-classification
language:
  - en
tags:
  - code
  - synthetic
  - code-review
  - static-analysis
pretty_name: Free Synthetic Code Diffs (100M)
size_categories:
  - 10M<n<100M

Free Synthetic Code Diffs (100M)

A 100-million-row synthetic dataset of code diffs labeled for destructive-change detection — the kind of change that silently drops data, tables, or columns in a migration or deploy. Every diff in this dataset is fabricated; none of it comes from real repositories, so there's no copyright or licensing entanglement.

Detecting a truly destructive change is harder than it looks. A plain regex for DROP TABLE or DELETE FROM catches the obvious cases and misses (or wrongly flags) a long tail of edge cases: destructive intent expressed through an ORM instead of raw SQL, casing or whitespace tricks that dodge a literal pattern match, a dangerous string sitting harmlessly inside a comment or log line, or a DROP that's actually just test-suite cleanup. This dataset is built specifically to stress-test detectors against that edge-case distribution, not just reward pattern recall.

Schema

Column	Type	Description
diff_id	string	Unique identifier for the diff
change_category	string	One of 7 categories (see below)
language	string	Source language of the changed file
file_path	string	Fabricated file path for the change
commit_message	string	Synthetic commit message
diff_text	string	The unified diff text
sql_pattern	string	SQL pattern present in the diff, if any
is_destructive_ground_truth	bool	Ground-truth label — whether the change is actually destructive

Change categories

• raw_sql_destructive (15%) — literal destructive SQL (DROP/DELETE/TRUNCATE)
• raw_sql_safe (15%) — literal SQL that looks risky but isn't destructive
• orm_schema_change (15%) — destructive change made through an ORM migration, no raw SQL to match on
• obfuscated_destructive (10%) — destructive SQL disguised via casing/whitespace/newline variation
• mentioned_in_comment (10%) — destructive-looking SQL sitting in a comment, docstring, or log string — not executed
• test_suite_cleanup (10%) — a DROP/DELETE inside test setup/teardown — not a production risk
• general_code_change (25%) — unrelated code changes, for class balance

is_destructive_ground_truth is set independently of whether a literal SQL keyword is present, so the dataset actually tests classification quality rather than keyword matching.

Format

Single Parquet file, Snappy compression, ~2.9 GB, 100,000,000 rows.

Quick start

import pandas as pd
df = pd.read_parquet("synthetic_code_diffs_100M.parquet")

from datasets import load_dataset
ds = load_dataset("ziadatalabs/FreeSyntheticCodeDiffs100M")

import duckdb
con = duckdb.connect()
con.sql("SELECT * FROM 'synthetic_code_diffs_100M.parquet' LIMIT 10").show()

Notes

• All diffs, file paths, and commit messages are fabricated — no real repository content.
• Categories are stratified as listed above; use change_category to filter or rebalance for your task.
• is_destructive_ground_truth is the label to train/evaluate against — don't infer it purely from sql_pattern.

License & Usage

Released under CC BY-NC 4.0 — free for personal, research, and educational use, with attribution. Not licensed for commercial use.

No real people, repositories, or organizations are represented in this data. It is entirely synthetic.

---

Created by Zia Data Labs. Questions or feedback: zia.data.team@protonmail.com