BlazingCustoms/pybytecode-csn-3.12-licensed

Data card — CSN-3.12-licensed decompilation benchmark

600 real Python functions from GitHub, compiled to Python 3.12 bytecode, each paired with the source that produced it. Every row carries full provenance and a licence resolved at the exact commit. Built 2026-08-04 by tools/build_csn_licensed.py.

Redistributable, provided NOTICES.md ships alongside. That file is not optional: MIT, BSD and Apache-2.0 require the notice to travel with the copy.

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The set

Rows	600
Source repositories	117
Max rows from any one repo	6 (1.00%)
Median rows per repo	6
Distinct commit SHAs	117
Rows with complete provenance	600 / 600
Rows with a resolved SPDX identifier	600 / 600
Disassembly length	mean 87.3 lines, median 59, max 1,622
Rows with annotations	63
Python	3.12, optimize=0

Licences, all resolved at the row's own commit:

SPDX	rows	share
MIT	391	65.2%
BSD-3-Clause	118	19.7%
Apache-2.0	64	10.7%
BSD-2-Clause	15	2.5%
ISC	12	2.0%

Why this replaces the previous 400-row set

The first CSN benchmark drew 400 rows from 24 repositories with 15% from one (Karaage-Cluster/python-tldap), recorded no commit SHA, and captured no licence. Auditing it afterwards: 180 of its 400 rows (45%) cannot be redistributed — 83 GPL-3.0 (including all 60 from that top repository) and 97 from repositories publishing no licence at all. Concentration at that level also means per-repository idiosyncrasy was a large fraction of what the benchmark measured. Details in ../../LICENSING-DETERMINATION.md §2.

The cap was set at 6 rows per repo — about 1% of the set — chosen so that no single codebase can move the headline number by more than a rounding error, while still admitting enough rows per repo to be worth resolving a licence for. At n=600 the 95% confidence interval on an accuracy near 93% is roughly ±2.0 points, against ±2.5 at n=400.

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Per-row fields

{
  "i": 0,
  "input":  "CODE <module>()\n  RESUME 0\n ...",   // model input: normalised disassembly
  "expected": "def f(x):\n    ...",                 // reference source
  "src_path": "src/00000.py",                       // RELATIVE to this directory
  "pyc_path": "pyc/00000.pyc",
  "provenance": {
    "repo": "owner/name",
    "repo_url": "https://github.com/owner/name",
    "file_path": "pkg/module.py",                   // path within the repo
    "commit_sha": "1b38e7cd...",                    // the commit the function was taken at
    "func_name": "Class.method",
    "line_start": 323, "line_end": 372,
    "permalink": "https://github.com/.../blob/<sha>/pkg/module.py#L323-L372",
    "dataset": "code-search-net/code_search_net (python, train split)"
  },
  "license": {
    "spdx": "BSD-3-Clause",
    "file": "LICENSE", "blob_sha": "3d9cc0c2...",
    "resolved_at_commit": "1b38e7cd...",
    "resolved_via": "GitHub GET /repos/{repo}/license?ref={sha}"
  },
  "n_instr": 49, "has_annotation": false
}

Paths are relative, so the set works from any checkout location. Nothing points at /tmp.

How rows were selected

Source: code-search-net/code_search_net, python train split, read in file order from a local parquet copy (tools/fetch_csn.sh; sha256 ad9e3a4ab10c2c1d8926d2b26ca2bfcc3aadda1477ba29a933391f93806b9fed).

3,138 rows were read to keep 600. In order:

Gate	Effect
Repo cap (6)	1,838 rows skipped — the mechanism that spreads the set across 117 repos
Repo blacklisted on licence	668 rows skipped, from the 25 repos rejected below
Licence not permissive / not resolvable	25 repos rejected
Train-contamination (identifier-blind fingerprint)	3 rows dropped
Unparseable	4 rows dropped

Repos rejected on licence: GPL-3.0 ×8, no licence file at that commit ×7, AGPL-3.0 ×3, LGPL-3.0 ×2, GPL-2.0 ×2, NOASSERTION ×2, LGPL-2.1 ×1.

A licence is resolved, never guessed. GET /repos/{owner}/{repo}/license?ref={sha} must return a concrete SPDX identifier on the allowlist (tools/resolve_licenses.py:ALLOWED_SPDX). NOASSERTION and Other count as unresolved and the row is dropped. We never infer a licence from a README, a setup.py classifier, or a package index.

Normalisation

Each function is reduced to a normal form before compiling: every docstring is replaced by the literal 'pass', then the tree is re-emitted with ast.unparse. Idempotence is checked per row and a row that is not a fixed point is dropped.

This build uses tools/normalize.py, a stdlib-only implementation. The GPL-3.0 tool that built the previous benchmark is not in this pipeline. The two agree byte-for-byte on 400/400 rows of the previous benchmark (tools/verify_equivalence.py); the reasoning is in ../../LICENSING-DETERMINATION.md §1.

Because docstrings are normalised away, this benchmark measures nothing about docstring recovery — samples_with_real_docstring is 0 by construction. Use the held-out set for that.

Compilation

py_compile at optimize=0 for the reference .pyc, and compile(..., dont_inherit=True, optimize=0) for the model's input. dont_inherit is deliberate: the training corpus was compiled with PEP-563 stringised annotations inherited, and real .pyc files are not. Handing the model its training-time distribution would flatter it, so we do not. See ../../DATA-CARD-training-corpus.md §7.

Harness soundness on this set

grade.py --self-test-only: pre-flight 600/600 = 100%, mutation kill rate 116/116 = 100%, 0 survivors. Both are prerequisites for quoting any score — but note that pre-flight is trivial by construction and proves nothing about soundness (it compares compile(x) with compile(x)). See ../../ORACLE-LIMITS.md §1.

Unit size — read this before comparing scores against another benchmark

An aggregate on this set is largely a statement about small units, so the size distribution is part of the result rather than a footnote to it. Size is measured in representation lines — the number of lines in the input field, i.e. the disassembly text a model is actually given.

min	p25	median	p75	p90	p99	max
19	39	59	101	167	478	1,622

Certification falls off sharply with size (v3 greedy, this set; intervals omitted below 30 rows / 10 repositories rather than printed at a misleading width):

rep lines	rows	v3 greedy	95% CI	v3 best-of-32	95% CI
0–49	224	95.98%	[93.01, 98.51]	98.21%	[96.31, 99.58]
50–99	224	89.73%	[85.17, 93.93]	98.21%	[96.26, 99.57]
100–199	112	65.18%	[55.36, 74.14]	85.71%	[77.57, 92.98]
200–299	27	51.85%	—	77.78%	—
300–399	5	60.00%	—	100.00%	—
400–599	5	0.00%	—	0.00%	—
600+	3	0.00%	—	0.00%	—

96.64% of v3's greedy certifications on this set (95.37% at best-of-32) come from units under 200 rep lines, and above ~400 rep lines nothing certified even at 32 samples. The 300–399 bucket reading above the one below it is n=5 noise, not a recovery. Recompute all of it with harness/size_curve.py; the stored output is ../../results/size_curve_csn600.json.

Limitations

• Measured scores (2026-08-04, L1 strict oracle): PyBytecode v3 greedy 506/600 = 84.33%, repo-clustered 95% CI [80.48, 87.94]; verified best-of-32 562/600 = 93.67%, CI [90.86, 96.08]. Untuned Qwen2.5-Coder-1.5B-Instruct control: 4/600 = 0.67%, CI [0.16, 1.35]. Per-row verdicts: ../../results/rows_with_base.jsonl.
• GitHub's licence detection is repository-level. A repository can vendor third-party files under other terms; we do not detect that, and no automated tool reliably does.
• A licence resolved at a commit is the licence of that repository snapshot. It does not prove the specific file was contributed under it.
• CodeSearchNet is a 2019-era snapshot, so the code skews toward pre-3.7 idiom. It contains no match statements, no walrus operators, and few modern typing constructs.
• Rows are capped, not sampled uniformly at random: the set is the first 6 admissible functions per repository in dataset order. That is reproducible and unbiased with respect to difficulty, but it is not a random sample of Python.
• 117 repositories is enough to defeat single-repo dominance; it is not a representative sample of the language ecosystem.

Provenance and licence of this benchmark

Derived from CodeSearchNet (code-search-net/code_search_net). The underlying functions remain under their original licences and copyright, listed per row and reproduced in full in NOTICES.md (117 repositories, 272 KB of licence text). The assembly, normalisation and disassembly are ours.