R-SET NOT_EQUAL
california_schools · mini_dev_postgresql from https://bird-bench.oss-cn-beijing.aliyuncs.com/minidev.zip (sha256 cc48ba16838204e4e214512030cb572eeb5f7bcdd999bae4b9b6ff12ec13b92f, downloaded 2026-09-07), member minidev/MINIDEV/mini_dev_postgresql.json
What is the average writing score of each of the schools managed by Ricci Ulrich? List the schools and the corresponding average writing scores.
the hint the set supplies: Usually, administrators manage the school stuff.
NOT_EQUAL states that these two statements disagree on this data under this rule. It does not state which of them is wrong.
read by hand, 2026-09-04: C the tool's rule, not the question's: the same number as int8 against numeric or float8 against numeric
AvgScrWrite as int8 against AVG() as numeric, one row, same value 507
a maintainer's reading of this question, out of classification.json, copied from plans/reports/prediction-mode-260904-real-predictions/classification.json. It is not a verdict and nothing above it was computed from it.
The two statements name their result columns differently. A projection is compared by position and declared type, so these names did not decide the verdict. They are part of the canonical rendering each result is hashed under, so the two result hashes differ with them.
SELECT T2.School, T1.AvgScrWrite FROM satscores AS T1 INNER JOIN schools AS T2 ON T1.cds = T2.CDSCode WHERE T2.AdmFName1 = 'Ricci' AND T2.AdmLName1 = 'Ulrich'this statement states no ordering of its own
sha256:5aaa13610c21a5b43270491b60a6c6e6803ae9956043afd27ac2912527e4e88c
SELECT s.sname, AVG(s.avgscrwrite) AS "Average Writing Score"
FROM satscores s
INNER JOIN schools sc ON s.cds = sc.cdscode
WHERE sc.admfname1 = 'Ricci' AND sc.admlname1 = 'Ulrich'
GROUP BY s.sname
ORDER BY s.sname ASCsha256:56a0f143d57fe8a3ceb599ec1a69038efcec36067ada1c68f12ccc35dead364c
The marked tokens are where the two texts differ. Two statements that differ everywhere can return the same rows, and two that differ in one token can return other rows; the verdict above is read off the results.
from counterexample.json, 1 row, up to 25 shown per side
| side | schooltext | avgscrwriteint8 |
|---|---|---|
| gold | Buchanan High | 507 |
from counterexample.json, 1 row, up to 25 shown per side
| side | snametext | Average Writing Scorenumeric |
|---|---|---|
| second | Buchanan High | 507.0000000000000000 |
set(second_rows) == set(gold_rows), float4/float8 cells as Python float as psycopg2 returns them, numeric as Decimal
https://github.com/bird-bench/mini_dev/blob/main/evaluation/evaluation_ex.py
result_eq: equal row counts and equal column counts, each row unordered as a quick rejection, then the two equal as a list when the gold text holds ORDER BY and as a multiset otherwise, under some permutation of the columns; DISTINCT is not stripped and re-executed, and the cells are PostgreSQL's as psycopg2 returns them
ruiqi-zhong/test-suite-sql-eval, exec_eval.py, result_eq, at commit 48cb78ec: https://github.com/ruiqi-zhong/test-suite-sql-eval/blob/48cb78ecf7f610620206283846c76751b18a1326/exec_eval.py
The class states what makes these two results unequal under this rule, read off the two results and nothing else. It does not state which of the two statements is wrong.
from counterexample.json, 1 row
| schooltext | avgscrwriteint8 |
|---|---|
| Buchanan High | 507 |
from counterexample.json, 1 row
| snametext | Average Writing Scorenumeric |
|---|---|
| Buchanan High | 507.0000000000000000 |
A smell is a mechanical reason to read this gold statement again. It is a heuristic: it does not state that the statement is wrong, and a maintainer decides.
this statement orders by a text column holding only numbers, and ordering it as a number gives a different answer, so the gold may be sorting 9.5 above 10
the statement states no top level ORDER BY
{
"heuristic": true,
"reason": "the statement states no top level ORDER BY"
}this statement cuts its result at a LIMIT that does not decide which rows come back, so a different but equally correct statement can return other rows and score zero
the statement states no LIMIT
{
"heuristic": true,
"reason": "the statement states no LIMIT"
}rerun over the same rows in another physical order this statement gives another answer, so its result depends on how the rows are stored and not only on the data
{
"heuristic": true,
"rule": "R-SET",
"baseline_result_hash": "sha256:5aaa13610c21a5b43270491b60a6c6e6803ae9956043afd27ac2912527e4e88c",
"baseline_result": {
"columns": [
{
"name": "school",
"declared_type": "text"
},
{
"name": "avgscrwrite",
"declared_type": "int8"
}
],
"row_count": 1,
"truncated": false,
"rows_shown": 1,
"rows": [
[
{
"type": "str",
"value": "Buchanan High"
},
{
"type": "int",
"value": 507
}
]
],
"result_hash": "sha256:5aaa13610c21a5b43270491b60a6c6e6803ae9956043afd27ac2912527e4e88c"
},
"planner_statistics": {
"satscores": {
"last_analyze": null,
"last_autoanalyze": "2026-09-08 05:19:51.562503+00",
"n_mod_since_analyze": 0
},
"schools": {
"last_analyze": null,
"last_autoanalyze": "2026-09-08 05:19:52.240861+00",
"n_mod_since_analyze": 0
}
},
"shuffle": {
"seed": "1",
"row_limit": 300000,
"tables": [
"satscores",
"schools"
],
"tables_not_shuffled": [],
"tables_skipped_for_size": {
"laptimes": 400524,
"legalities": 427907,
"posthistory": 303155,
"trans": 1056320,
"yearmonth": 383282
},
"tables_not_reached_by_a_copy": {}
},
"shuffled_copies": {
"run": true,
"verdict": "equal",
"differs": false,
"result_hash": "sha256:5aaa13610c21a5b43270491b60a6c6e6803ae9956043afd27ac2912527e4e88c",
"result": {
"columns": [
{
"name": "school",
"declared_type": "text"
},
{
"name": "avgscrwrite",
"declared_type": "int8"
}
],
"row_count": 1,
"truncated": false,
"rows_shown": 1,
"rows": [
[
{
"type": "str",
"value": "Buchanan High"
},
{
"type": "int",
"value": 507
}
]
],
"result_hash": "sha256:5aaa13610c21a5b43270491b60a6c6e6803ae9956043afd27ac2912527e4e88c"
}
},
"plan_variant": {
"run": false,
"reason": "the plan variant was not asked for"
}
}SELECT T2.School, T1.AvgScrWrite FROM satscores AS T1 INNER JOIN schools AS T2 ON T1.cds = T2.CDSCode WHERE T2.AdmFName1 = 'Ricci' AND T2.AdmLName1 = 'Ulrich'result_hash sha256:5aaa13610c21a5b43270491b60a6c6e6803ae9956043afd27ac2912527e4e88c recomputed from this JSON: match
record_hash sha256:f0eca6fc798458ffce2aed67962af8e4705c60462e6e35b0372c82781308cce4 recomputed from this JSON: match
from evidence-gold.json, 1 row
| schooltext | avgscrwriteint8 |
|---|---|
| Buchanan High | 507 |
SELECT s.sname, AVG(s.avgscrwrite) AS "Average Writing Score"
FROM satscores s
INNER JOIN schools sc ON s.cds = sc.cdscode
WHERE sc.admfname1 = 'Ricci' AND sc.admlname1 = 'Ulrich'
GROUP BY s.sname
ORDER BY s.sname ASCresult_hash sha256:56a0f143d57fe8a3ceb599ec1a69038efcec36067ada1c68f12ccc35dead364c recomputed from this JSON: match
record_hash sha256:d43cb9af46c02e9bfda0f3fa15aa86ef5235b77e9d5b237018cd55f67a95b82e recomputed from this JSON: match
from evidence-second.json, 1 row
| snametext | Average Writing Scorenumeric |
|---|---|
| Buchanan High | 507.0000000000000000 |
re-run this statement read-only against PostgreSQL 16.15 (Debian 16.15-1.pgdg13+2) on aarch64-unknown-linux-gnu, compiled by gcc (Debian 14.2.0-19) 14.2.0, 64-bit | server=172.17.0.2/32:5432 | database=bird under the session settings and over the data this record's fixture digest names, and compare the two results under R-SET
re-run this statement read-only against PostgreSQL 16.15 (Debian 16.15-1.pgdg13+2) on aarch64-unknown-linux-gnu, compiled by gcc (Debian 14.2.0-19) 14.2.0, 64-bit | server=172.17.0.2/32:5432 | database=bird under the session settings and over the data this record's fixture digest names, and compare the two results under R-SET