SalutefaceAAC/sql-dataset-coverage-analysis

Coverage Analysis: SQL Natural Language Pairs Synthetic Benchmark

Technical Report · October 2026 · SaluteFace AAC
Not peer reviewed. Computed on SalutefaceAAC/sql-natural-language-pairs v1.0.

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Abstract

Synthetic text-to-SQL datasets are useful for fine-tuning and evaluation, but require principled quality assessment beyond simple scale metrics. We present a distributional coverage analysis of the sql-natural-language-pairs dataset using four complementary dimensions: lexical diversity, SQL feature coverage, domain balance, and difficulty calibration. We find the dataset achieves near-perfect domain balance (Shannon balance 0.96), zero exact duplicates, and well-calibrated difficulty (SQL token length scales monotonically: easy 10.0 → medium 25.5 → hard 35.8 tokens). Key gaps include question-starter concentration (four starters cover 80% of questions), underrepresentation of MAX/MIN/COALESCE aggregate functions, and a domain skew toward ecommerce (34.3%). We compute a Composite Coverage Score of 0.78 / 1.0 and provide targeted gap-filling recommendations.

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1. Dataset Under Analysis

Property	Value
Dataset	SalutefaceAAC/sql-natural-language-pairs
Total examples	286
Splits	230 train / 28 val / 28 test
Domains	ecommerce, healthcare, analytics, hr, inventory
Query types	aggregate, join, select, subquery, window, cte
Difficulty tiers	easy, medium, hard
License	CC-BY-4.0

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2. Methodology

All metrics are computed from the raw dataset using the HuggingFace datasets library. The analysis covers four dimensions:

1. Lexical Diversity — Distinct-N metrics, type-token ratio, near-duplicate detection via Jaccard similarity
2. SQL Feature Coverage — Keyword frequency for 12 SQL clauses and 7 aggregate functions
3. Domain Balance — Shannon entropy normalized to max entropy for 5 domains
4. Difficulty Calibration — SQL token count distribution per difficulty tier, query-type × difficulty cross-tabulation

Composite Coverage Score = arithmetic mean of four sub-scores (Deduplication, Domain Balance, Feature Coverage, Phrasing Diversity).

Analysis code:

from datasets import load_dataset
import pandas as pd, re, math
from collections import Counter

ds = load_dataset('SalutefaceAAC/sql-natural-language-pairs')
dfs = [ds[s].to_pandas().assign(split=s) for s in ds]
df = pd.concat(dfs, ignore_index=True)

def tokenize(text):
    return re.findall(r'\b\w+\b', text.lower())

# Distinct-N
all_toks = [t for q in df['question'] for t in tokenize(q)]
distinct1 = len(set(all_toks)) / len(all_toks)

bigrams = [(a,b) for q in df['question'] for a,b in zip(tokenize(q), tokenize(q)[1:])]
distinct2 = len(set(bigrams)) / len(bigrams)

# Domain balance (Shannon entropy)
p = df['domain'].value_counts() / len(df)
H = -sum(x * math.log(x) for x in p)
balance = H / math.log(len(p))

print(f"Distinct-1: {distinct1:.3f}, Distinct-2: {distinct2:.3f}, Balance: {balance:.3f}")

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3. Results

3.1 Lexical Diversity

Metric	Value	Assessment
Distinct-1 (unigrams)	0.157	Expected low for short domain-specific questions
Distinct-2 (bigrams)	0.498	Moderate — half of bigrams are unique
Type-token ratio	0.157	Narrow domain vocabulary
Avg question length	8.8 tokens	Concise and focused
Question length range	4–16 tokens	—
Exact duplicates	0	✅ Clean
Near-duplicate rate (Jaccard > 0.7)	0.5%	✅ Excellent

Question starter distribution (concentration risk: four starters = 80.3% of questions):

Starter	Count	%
what	67	23.4%
find	59	20.6%
how	57	19.9%
list	47	16.4%
show	17	5.9%
which	14	4.9%
other (6 types)	25	8.7%

3.2 SQL Feature Coverage

Clause frequency:

SQL Clause	Count	%	Status
WHERE	199	69.6%	✅ Strong
JOIN	109	38.1%	✅ Good
ORDER BY	86	30.1%	✅ Good
GROUP BY	72	25.2%	✅ Good
LIMIT	30	10.5%	✅ OK
Subquery	27	9.4%	✅ OK
DISTINCT	17	5.9%	⚠️ Low
OVER (window)	15	5.2%	⚠️ Low
HAVING	11	3.8%	⚠️ Low
WITH (CTE)	9	3.1%	🔴 Gap
LEFT JOIN	8	2.8%	🔴 Gap
CASE	4	1.4%	🔴 Gap

Aggregate function coverage:

Function	Count	%	Status
COUNT	87	30.4%	✅ Good
SUM	52	18.2%	✅ Good
AVG	43	15.0%	✅ Good
MIN	5	1.7%	⚠️ Low
MAX	3	1.0%	🔴 Gap
NULLIF	2	0.7%	🔴 Gap
COALESCE	0	0.0%	🔴 Missing

3.3 Domain Balance

Shannon balance score: 0.9603 (1.0 = perfectly balanced across 5 domains)

Domain	Count	%	Distance from ideal 20%
ecommerce	98	34.3%	+14.3% (overrepresented)
healthcare	59	20.6%	+0.6%
analytics	48	16.8%	−3.2%
hr	42	14.7%	−5.3%
inventory	39	13.6%	−6.4% (underrepresented)

Domain × Difficulty cross-tabulation:

Domain	Easy	Medium	Hard	Total
ecommerce	52	32	14	98
healthcare	30	18	11	59
analytics	24	13	11	48
hr	17	15	10	42
inventory	19	12	8	39

All domains have non-zero representation at all difficulty levels — no structural gaps.

3.4 Difficulty Calibration

Difficulty	Count	Avg SQL tokens	Min	Max
easy	142	10.0	5	29
medium	90	25.5	8	48
hard	54	35.8	12	82

SQL length increases monotonically (easy < medium < hard). Query type × difficulty cross-tab shows logically consistent assignment:

Query Type	Easy	Medium	Hard
aggregate	88	36	6
select	48	6	0
join	6	46	4
subquery	0	2	23
window	0	0	12
cte	0	0	9

SELECT is easy-only; subquery, window, and cte are concentrated in hard. This structure is semantically sound.

3.5 Composite Coverage Score

Dimension	Score	Basis
Deduplication	0.99	Near-duplicate rate 0.5%
Domain Balance	0.96	Shannon entropy / H_max
Feature Coverage	0.71	7/10 target clauses ≥5%
Phrasing Diversity	0.67	6/9 target starters covered
Composite (mean)	0.78	—

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4. Key Findings

F1 — Difficulty is well-calibrated. SQL token count increases monotonically: 10.0 → 25.5 → 35.8. Query type assignments are logically consistent (SELECT → easy, CTE/window → hard). This is the strongest quality signal in the dataset.

F2 — Domain balance is near-ideal. Shannon balance of 0.96 confirms all five domains are meaningfully represented at all difficulty levels. The only outlier is ecommerce at 34.3%.

F3 — Deduplication is effective. Zero exact duplicates. Near-duplicate rate (Jaccard > 0.7) of 0.5% across 1,000 random pairs — effectively zero given the domain-specific short vocabulary.

F4 — Question phrasing is concentrated. Four starters (what/find/how/list) cover 80.3% of questions. Models fine-tuned on this data may underperform on imperative commands ("calculate", "retrieve") or declarative framings ("the total revenue is...").

F5 — Aggregate function gaps exist. MAX (1.0%), MIN (1.7%), and COALESCE (0.0%) are severely underrepresented relative to COUNT (30.4%) and SUM (18.2%). This could cause fine-tuned models to default to COUNT/SUM even when MAX/MIN is correct.

F6 — Complex patterns are sparse. CTEs (3.1%, n=9) and window functions (4.2%, n=12) are underrepresented for their practical importance in enterprise SQL. LEFT JOIN (2.8%) and CASE (1.4%) are also gap areas.

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5. Limitations

1. No execution validation. SQL correctness is assessed structurally (keyword presence, token counts), not by running against a populated database. Syntactically correct queries may produce incorrect results.

2. Jaccard as near-duplicate metric. Jaccard similarity on token sets misses semantic similarity. Two questions phrased differently but meaning the same thing count as distinct even when the SQL is identical.

3. Token-length as complexity proxy. SQL token count correlates with complexity but doesn't capture semantic depth (e.g., a short correlated subquery may be harder than a long flat aggregate).

4. Distinct-N interpretation. Distinct-1 of 0.157 should not be interpreted as a quality problem. For domain-specific questions averaging 8.8 tokens, vocabulary reuse is expected. Contextualize against the near-duplicate rate (0.5%).

5. Informal coverage score. The Composite Coverage Score is a heuristic constructed for this analysis. It is not a validated benchmark metric and should not be used for cross-dataset comparisons without re-grounding the sub-scores.

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6. Recommendations for v2

Short term (immediate):

• Add 10–15 examples each for MAX, MIN, COALESCE
• Add question starters: calculate, retrieve, identify, compare, determine
• Balance inventory domain toward 55 examples (currently 39)

Medium term:

• Expand CTEs to 25+ examples across all domains
• Add LEFT JOIN, FULL OUTER JOIN, and CASE WHEN patterns
• Add multi-condition HAVING clauses

Long term:

• Execution-validate all queries against DuckDB/SQLite with synthetic data
• Generate 3–5 paraphrase variants per SQL query
• Scale to 1,000+ examples with v2 tag

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References

• Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task — Yu et al., 2018
• WikiSQL: A Large Annotated Semantic Parsing Corpus for Developing Natural Language Interfaces to Relational Databases — Zhong et al., 2017
• BIRD: A Big Bench for Large-Scale Database Grounded Text-to-SQLs — Li et al., 2023
• A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directions — Qin et al., 2022
• Distinct-N metric for text diversity — Li et al., 2016

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Citation

@techreport{saluteface_sql_coverage_2026,
  title  = {Coverage Analysis: SQL Natural Language Pairs Synthetic Benchmark},
  author = {SaluteFace AAC},
  year   = {2026},
  month  = {10},
  note   = {Technical report. Not peer reviewed. Computed on SalutefaceAAC/sql-natural-language-pairs v1.0.},
  url    = {https://huggingface.co/datasets/SalutefaceAAC/sql-dataset-coverage-analysis}
}

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