Techniques for learning logic programs from data typically rely on language bias mechanisms to restrict the hypothesis space. These methods are therefore limited by the user's ability to tune them such that the hypothesis space is simultaneously large enough to include the target program but small enough to admit a tractable search. We propose a technique to learn Datalog programs from input-output examples without requiring the user to specify any language bias. It employs an evolutionary search strategy that mutates candidate programs and evaluates their fitness on the examples using an off-the-shelf Datalog interpreter. We have implemented our approach in a tool called GenSynth and evaluate it on diverse tasks from knowledge discovery, program analysis, and relational queries. Our experiments show that GenSynth can learn correct programs from few examples, including for tasks that require recursion and invented predicates, and is robust to noise.

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.

We propose a new approach to synthesize Datalog programs from input-output specifications. Our approach leverages query provenance to scale the counterexample-guided inductive synthesis (CEGIS) procedure for program synthesis. In each iteration of the procedure, a SAT solver proposes a candidate Datalog program, and a Datalog solver evaluates the proposed program to determine whether it meets the desired specification. Failure to satisfy the specification results in additional constraints to the SAT solver. We propose efficient algorithms to learn these constraints based on “ why ” and “ why not ” provenance information obtained from the Datalog solver. We have implemented our approach in a tool called ProSynth and present experimental results that demonstrate significant improvements over the state-of-the-art, including in synthesizing invented predicates, reducing running times, and in decreasing variances in synthesis performance. On a suite of 40 synthesis tasks from three different domains, ProSynth is able to synthesize the desired program in 10 seconds on average per task—an order of magnitude faster than baseline approaches—and takes only under a second each for 28 of them.