Synthesizing relational queries from data is challenging in the presence of recursion and invented predicates. We propose a fully automated approach to synthesize such queries. Our approach comprises of two steps: it first synthesizes a non-recursive query consistent with the given data, and then identifies recursion schemes in it and thereby generalizes to arbitrary data. This generalization is achieved by an iterative predicate unification procedure which exploits the notion of data provenance to accelerate convergence. In each iteration of the procedure, a constraint solver proposes a candidate query, and a query evaluator checks if the proposed program is consistent with the given data. The data provenance for a failed query allows us to construct additional constraints for the constraint solver and refine the search. We have implemented our approach in a tool named Mobius. On a suite of 21 challenging recursive query synthesis tasks, Mobius outperforms three state-of-the-art baselines Gensynth, ILASP, and Popper, both in terms of runtime and accuracy. We also demonstrate that the synthesized queries generalize well to unseen data.

Program synthesis tasks are commonly specified via input-output examples. Existing enumerative techniques for such tasks are primarily guided by program syntax and only make indirect use of the examples. We identify a class of synthesis algorithms for programming-by-examples, which we call Example-Guided Synthesis (EGS), that exploits latent structure in the provided examples while generating candidate programs. We present an instance of EGS for the synthesis of relational queries and evaluate it on 86 tasks from three application domains: knowledge discovery, program analysis, and database querying. Our evaluation shows that EGS outperforms state-of-the-art synthesizers based on enumerative search, constraint solving, and hybrid techniques in terms of synthesis time, quality of synthesized programs, and ability to prove unrealizability.

There has been widespread adoption of IDEs and powerful tools for program analysis. However, programmers still find it difficult to conveniently analyze their code for custom patterns. Such systems either provide inflexible interfaces or require knowledge of complex query languages and compiler internals. In this paper, we present Sporq, a tool that allows developers to mine their codebases for a range of patterns, including bugs, code smells, and violations of coding standards. Sporq offers an interactive environment in which the user highlights program elements, and the system responds by identifying other parts of the codebase with similar patterns. The programmer can then provide feedback which enables the system to rapidly infer the programmer’s intent. Internally, our system is driven by high-fidelity relational program representations and algorithms to synthesize database queries from examples. Our experiments and user studies with a VS Code extension indicate that Sporq reduces the effort needed by programmers to write custom analyses and discover bugs in large codebases.