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This paper presents a new technique for automatically synthesizing SQLqueries from natural language. Our technique is fully automated, works for anydatabase without requiring additional customization, and does not require usersto know the underlying database schema. Our method achieves these goals bycombining natural language processing, program synthesis, and automated programrepair. Given the user's English description, our technique first uses semanticparsing to generate a query sketch, which is subsequently completed usingtype-directed program synthesis and assigned a confidence score using databasecontents. However, since the user's description may not accurately reflect theactual database schema, our approach also performs fault localization andrepairs the erroneous part of the sketch. This synthesize-repair loop isrepeated until the algorithm infers a query with a sufficiently high confidencescore. We have implemented the proposed technique in a tool called Sqlizer andevaluate it on three different databases. Our experiments show that the desiredquery is ranked within the top 5 candidates in close to 90% of the cases.

Type- and Content-Driven Synthesis of SQL Queries from Natural Language