Trace-Guided Inductive Synthesis of Recursive Functional Programs
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 m...
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| Veröffentlicht in: | Proceedings of ACM on programming languages Jg. 7; H. PLDI; S. 860 - 883 |
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| 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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| AbstractList | 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. |
| ArticleNumber | 141 |
| Author | Radhakrishna, Arjun Yuan, Yongwei Samanta, Roopsha |
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| Cites_doi | 10.1145/2737924.2737952 10.1145/3408991 10.1145/2908080.2908093 10.1145/2908080.2908102 10.1145/2837614.2837666 10.1007/978-3-030-81685-8_39 10.1145/3290327 10.1145/3133886 10.1145/2737924.2737977 10.1145/3547622 10.1145/2980983.2908103 10.1145/3434335 10.1007/978-3-662-54577-5_18 10.1145/1806596.1806632 10.1145/1926385.1926423 10.1145/2509136.2509555 10.1145/3498682 10.1145/2983990.2984030 10.1145/3428295 10.1145/322217.322228 10.1007/3-540-58085-9_72 10.1109/ICSE.2017.44 10.5281/zenodo.7812616 10.1145/1706356.1706364 10.1145/3571263 10.1145/321992.322002 10.1145/321420.321422 10.1145/3158151 10.1007/978-3-030-25540-4_17 10.1145/2814270.2814310 10.1145/3290386 10.1016/0004-3702(82)90040-6 10.1145/2737924.2738007 10.1023/A:1025671410623 10.1145/2594291.2594333 10.1145/3453483.3454087 10.1007/978-3-642-39799-8_67 10.1007/978-3-319-21668-3_10 10.1145/3519939.3523726 10.1145/1706299.1706339 10.24963/ijcai.2017/227 10.1145/3485544 10.1145/3276525 |
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| Title | Trace-Guided Inductive Synthesis of Recursive Functional Programs |
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