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Database systems are widely used to store and query data. Test oracles havebeen proposed to find logic bugs in such systems, that is, bugs that cause thedatabase system to compute an incorrect result. To realize a fully automatedtesting approach, such test oracles are paired with a test case generationtechnique; a test case refers to a database state and a query on which the testoracle can be applied. In this work, we propose the concept of Query PlanGuidance (QPG) for guiding automated testing towards "interesting" test cases.SQL and other query languages are declarative. Thus, to execute a query, thedatabase system translates every operator in the source language to one ofpotentially many so-called physical operators that can be executed; the tree ofphysical operators is referred to as the query plan. Our intuition is that bysteering testing towards exploring diverse query plans, we also explore moreinteresting behaviors-some of which are potentially incorrect. To this end, wepropose a mutation technique that gradually applies promising mutations to thedatabase state, causing the DBMS to create diverse query plans for subsequentqueries. We applied our method to three mature, widely-used, andextensively-tested database systems-SQLite, TiDB, and CockroachDB-and found 53unique, previously unknown bugs. Our method exercises 4.85-408.48X more uniquequery plans than a naive random generation method and 7.46X more than a codecoverage guidance method. Since most database systems-including commercialones-expose query plans to the user, we consider QPG a generally applicable,black-box approach and believe that the core idea could also be applied inother contexts (e.g., to measure the quality of a test suite).

Testing Database Engines via Query Plan Guidance