
We present a novel approach for generating syntactically and semantically correct SQL queries as inputs for testing relational databases. We leverage the SAT-based Alloy tool-set to reduce the problem of generating valid SQL queries into a SAT problem. Our approach translates SQL query constraints into Alloy models, which enable it to generate valid queries that cannot be automatically generated using conventional grammar-based generators.Given a database schema, our new approach combined with our previous work on ADUSA, automatically generates (1) syntactically and semantically valid SQL queries for testing, (2) input data to populate test databases, and (3) expected result of executing the given query on the generated data.Experimental results show that not only can we automatically generate valid queries which detect bugs in database engines, but also we are able to combine this work with our previous work on ADUSA to automatically generate input queries and tables as well as expected query execution outputs to enable automated testing of database engines.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 30 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
