Shedding Light on Opaque Application Queries

We investigate a new query reverse-engineering problem of unmasking SQL queries hidden within database applications. The diverse use-cases for this problem range from resurrecting legacy code to query rewriting. As a first step in addressing the unmasking challenge, we present UNMASQUE, an active-learning extraction algorithm that can expose a basal class of hidden warehouse queries. A special feature of our design is that the extraction is non-invasive wrt the application, examining only the results obtained from repeated executions on databases derived with a combination of data mutation and data generation techniques. Further, potent optimizations are incorporated to minimize the extraction overheads. A detailed evaluation over applications hosting hidden SQL queries, or their imperative versions, demonstrates that UNMASQUE correctly and efficiently extracts these queries.