UNMASQUE: A Hidden SQL Query Extractor

Given a database instance and a populated result, query reverse-engineering attempts to identify candidate SQL queries that produce this result on the instance. A variant of this problem arises when a ground-truth is additionally available, but hidden within an opaque database application. In this demo, we present UN-MASQUE, an extraction algorithm that is capable of precisely identifying a substantive class of such hidden queries. A hallmark of its design is that the extraction is completely non-invasive to the application. Specifically, it only examines the results obtained from application executions on databases derived with a combination of data mutation and data generation techniques, thereby achieving platform-independence. Further, potent optimizations, such as database size reduction to a few rows, are incorporated to minimize the extraction overheads. The demo showcases these features on both declarative and imperative applications.