Datasets:

ykvlev
/

sreb

Published rewrite quality is measured by counts of successful-looking suggestions; we propose to measure it by the database states on which a suggestion is provably wrong, and we release the first dataset of such evidence.

1. Introduction

Rewriting SQL is common practice for performance; language models now suggest rewrites.
A rewrite can be syntactically valid, faster, and wrong: multiplicities collapse, NULL semantics shift, ordering or LIMIT is lost.
Timing benchmarks cannot see this. Formal checkers cover a subset of SQL.
Contribution: evidence-first evaluation, an open dataset with witnesses, a repro protocol.

2. Related work

Group	Examples	What it gives	What it does not
Formal equivalence	Cosette, VeriEQL, HoTTSQL	proofs for supported constructs	coverage of full SQL, per-run evidence
Rewrite generation	GenRewrite, LLM-R2, R-Bot, QUITE	systems, success rates	executable witnesses
Benchmarks	Spider, BIRD, TACO	text-to-SQL evaluation	rewrite validation
DBMS testing	Pivoted Query Synthesis, NoREC, SQLancer, QED	finding engine bugs	evaluating model rewrites

Positioning: SREB is not another text-to-SQL benchmark and not a formal prover. It is a record of what happened when a model's rewrite met a database.

3. What counts as evidence

Define the terminology the dataset uses:

allowed state — a database instance satisfying the declared schema and constraints.
state profile — the property a state is built to exercise (empty, nullable, multiplicity, boundary, ties, random).
refutation — reference and candidate return different results on an allowed state.
witness — the refuting state and the rows that differ, in a form a human can read. Witnesses are not minimised: no state-reduction procedure was run, so they are the first refuting state found, not the smallest one.

Explicitly: absence of refutation on a finite bank is not a proof of equivalence. The dataset must never imply otherwise.

4. Dataset construction

Schemas with explicit constraints; query families per schema.
Family = (schema, reference SQL, intended profile).
State generation: profile templates + seeded random instances, frozen before model runs.
Hashing: corpus and state bank sha256 frozen in manifest.json; a run with other hashes is invalid.
Model protocol: digest, quantization, temperature, context length, max tokens, one answer per query, no seed where the engine allows none.

5. Evaluation protocol

For each (family, model):

Parse the candidate; reject unsupported or unsafe statements.
For each allowed state: create a clean database, apply the state, run reference and candidate read-only, compare rows.
Record outcome: preserved / refuted / no_rewrite / unsupported / error.
If refuted, store the profile, the state seed and the row difference.

6. Pilot results (v0.2.0, numbers from `data/`)

The v0.2.0 corpus has 204 families on three schemas (library, shop, university); the evidence covers all 204 families, 28 states each, six models and 34 272 executed checks on MySQL 8.4.11 (120 refuted rewrites in 75 families).

The code-specialised models rewrote more often and were refuted more often than the general-purpose ones. deepseek-coder-v2 proposed 77 rewrites (27 refuted), qwen2.5-coder:7b 30 (12 refuted), qwen2.5:7b 32 (8 refuted), phi3.5:3.8b 30 (8 refuted, plus 15 unparsable answers). The two 3B models rewrote aggressively: qwen2.5:3b proposed 85 rewrites (39 refuted, 6 unparsable, 16 execution errors), llama3.2:3b proposed 53 (26 refuted, 1 unparsable, 10 execution errors).
Four of the six models independently produced the same wrong rewrite class (UNION ALL → OR or DISTINCT over the union), losing row multiplicities; union_bag and lib_union_bag_books were refuted by every one of those four models. Several families have an identical normalised wrong candidate produced by two or more models, e.g. UNION rewritten as OR or IN, which keeps duplicates that UNION removes.
The models fail in different ways on the same family: union_bag is broken by an OR rewrite in three models and by DISTINCT over UNION ALL in the fourth. Invalid SQL is also part of the record: missing derived-table aliases, ONLY_FULL_GROUP_BY violations, hallucinated clauses such as WITHIN ..., and unknown columns are stored as error/unsupported, not discarded.

7. Reproducibility

Every published number recomputable from data/ without rerunning a model.
Every run is traceable to its model digest, sampling parameters, DBMS version and the corpus/state-bank hashes pinned in its record. Model collection and evaluation scripts are not part of v0.2.0, so a rerun requires reproducing the recorded protocol.
Engine version is part of the record: behaviour changes across releases.

8. Limitations

Synthetic schemas: partial coverage of real workloads.
Finite state banks: no completeness claims.
One or two DBMS engines in the pilot.
Model sets age quickly; the dataset is designed to accept new models without breaking.

9. Availability

Repository, Zenodo DOI (version and concept), HuggingFace dataset card, leaderboard.

10. Open questions for the community

Which state profiles are worth standardising?
How should refutation rate be normalised against rewrite propensity?
Can formal methods consume the witnesses as seeds?

Checklist before any submission

every number in the text recomputed from data/
corpus and state bank hashes match manifest.json
model digests listed
limitations section says plainly that preserved ≠ proven
dataset DOI resolves to the released version, not to master
no personal data anywhere in the dataset

SREB paper — outline

Working title

The claim in one sentence