Trace-Guided Inductive Synthesis of Recursive Functional Programs - Yuan, Yongwei; Radhakrishna, Arjun; Samanta, Roopsha (Proceedings of ACM on programming languages, 2023)
We propose a novel trace-guided approach to tackle the challenges of ambiguity and generalization in synthesis of recursive functional programs from input-output examples. Our approach augments the search space of programs with recursion traces consisting of recursive subcalls of the programs. Our method is based on a new version space algebra (VSA) for succinct representation and efficient manipulation of pairs of recursion traces and programs that are consistent with each other. We have implemented this approach in a tool called SyRup and evaluated it on benchmarks from prior work. Our evaluation demonstrates that SyRup not only requires fewer examples to achieve a certain success rate than existing synthesizers, but is also less sensitive to the quality of the examples.