Mozi: Discovering DBMS Bugs via Configuration-Based Equivalent Transformation
Bibliographic record
Abstract
Testing database management systems (DBMSs) is a complex task. Traditional approaches, such as metamorphic testing, need a precise comprehension of the SQL specification to create diverse inputs with equivalent semantics. The vagueness and intricacy of the SQL specification make it challenging to accurately model query semantics, thereby posing difficulties in testing the correctness and performance of DBMSs. To address this, we propose Mozi, a framework that finds DBMS bugs via configuration-based equivalent transformation. The key idea behind Mozi is to compare the results of equivalent DBMSs with different configurations, rather than between semantically equivalent queries. The framework involves analyzing the query plan, changing configurations to transform the DBMS to an equivalent one, and re-executing the query to compare the results using various test oracles. For example, detecting differences in query results indicates correctness bugs, while observing faster execution times on the optimization-closed DBMS suggests performance bugs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".