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Trace-Guided Inductive Synthesis of Recursive Functional Programs

PLDI 7(PLDI)2023
Yongwei Yuan, Arjun Radhakrishna, Roopsha Samanta

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原文摘要(Abstract)

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.

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DOI 原文 ·

BibTeX
@article{paperbot1986,
  title = {Trace-Guided Inductive Synthesis of Recursive Functional Programs},
  author = {Yongwei Yuan and Arjun Radhakrishna and Roopsha Samanta},
  journal = {Proceedings of the ACM on Programming Languages},
  volume = {7},
  number = {PLDI},
  year = {2023},
  doi = {10.1145/3591255}
}