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dimsmauls/
code4ml-sq-filtered

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Code4ML with SonarQube Maintainability Ratings

Python code snippets from the Code4ML corpus (Zenodo record 7312803) — machine-learning notebooks harvested from Kaggle — each annotated with structural code-quality metrics produced by SonarQube static analysis.

Every code_block was scanned individually and joined back to the source snippet as an audit trail. The maintainability_rating (A–E) is the headline label; supporting raw metrics (code smells, complexity, bugs, technical debt) are kept for transparency.

Dataset summary


Records	2,599,355
Rated (non-empty `maintainability_rating`)	2,596,565 (99.89%)
Format	single comma-delimited CSV, one header
File	`code4ml_sonarqube_rated.csv` (~986 MB)
Language	Python

The ~0.11% unrated rows are snippets SonarQube could not analyze (empty or unparseable).

Columns

column	type	description
(unnamed index)	int	row index inherited from source shards
`kernel_id`	int	Kaggle kernel/notebook id
`code_block_id`	int	id of the code block within the kernel
`code_block`	string	the Python source snippet (may span multiple lines)
`sqale_rating`	float	raw SQALE rating (1.0 = A … 5.0 = E)
`maintainability_rating`	string	maintainability grade `A`–`E` (headline label)
`code_smells`	int	number of code smells
`complexity`	int	cyclomatic complexity
`cognitive_complexity`	int	cognitive complexity
`bugs`	int	number of bugs detected
`sqale_index`	int	technical-debt index (minutes)
`ncloc`	int	non-comment lines of code

Usage

python

from datasets import load_dataset

ds = load_dataset("<your-username>/code4ml-sonarqube-maintainability", split="train")
print(ds[0]["code_block"], ds[0]["maintainability_rating"])

Filter to a quality floor (keep A–C):

good = ds.filter(lambda r: r["maintainability_rating"] in {"A", "B", "C"})

How it was built

Split the source code_blocks.csv into 12 balanced shards (Rust CSV splitter).
Scan each snippet through SonarQube via a FastAPI wrapper + sonar-scanner.
Join the resulting id;maintainability_rating ratings back onto each shard (add-rating, LEFT JOIN on code_block_id).
Merge all 12 rated shards into this single CSV.

Provenance & attribution

Source corpus: Code4ML — Zenodo record 7312803.
Original code: the code_block snippets are Kaggle notebook cells, each published under the Apache-2.0 license (see per-kernel links in Code4ML's kernels_meta.csv). Cells were split and stripped of redundant comments/markdown; PEP8 fixes may have been applied. No code-affecting changes were made. See NOTICE.txt.
This derivative adds only the SonarQube static-analysis columns (maintainability rating + supporting metrics). If you use this dataset, please cite the original Code4ML work as well.

License

The dataset (compilation + SonarQube annotation columns) is released under CC-BY-4.0. See LICENSE.

The underlying code_block snippets are Kaggle notebook code, each under the Apache-2.0 license; that attribution is preserved in NOTICE.txt. Redistributing the Apache-2.0 code within this CC-BY-4.0 dataset is permitted as long as the NOTICE is kept.

dimsmauls/code4ml-sq-filtered