ParSEval: Plan-Aware Test Database Generation for SQL Equivalence Evaluation
Bibliographic record
Abstract
Deciding query equivalence has played an essential role in many real-world applications, including evaluating the accuracy of text-to-SQL models, where one needs to compare model-generated queries against ground truth queries. Although query equivalence is undecidable in general, researchers have developed two significant approaches to check query equivalence: formal verification-based and test-case-based. Verification-based solutions ensure correctness but may lack support for advanced SQL features and cross-database adaptability. Test cases are versatile but suffer from ad-hoc constraints and potential incorrectness (false positives). In this paper, we propose ParSEval, a Plan-aware SQL Equivalence evaluation framework to generate test database instances for given queries. We observed that existing test data generation methods fail to fully explore the query structure. To address this limitation, ParSEval formally models specific behaviors of each query operator and considers all possible execution paths of the logical query plan by adapting the notion of branch coverage. We validated the effectiveness and efficiency of ParSEval on four datasets with AI-generated and human-crafted queries. The experimental results show that ParSEval supports up to 40% more query pairs than state-of-the-art verification-based approaches. Compared to existing test-case-based approaches, ParSEval reveals more non-equivalent pairs while being 21× faster.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".