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- W4376606662 abstract "The pseudocode-to-code task has two main stages: code translation and search synthesis. It faces two main challenges: First, the generated candidate code pieces need to be more accurate. Second, there are problems with search efficiency and accuracy. To address the above challenges, this work proposes a novel approach: For the encoder of code translation, a new multi-scale pyramid feature extractor is proposed to obtain multi-scale local information, which is combined with the global information to improve the accuracy of code translation. For the search synthesis stage, this paper designs the intra-line attention, the inter-line attention, and the code-errMsg attention, which are adaptively integrated with the graph attention to effectively fuse global and local information. Under a budget of 100 program compilations, our final model, AGL-Code, outperforms the previous state-of-the-art models, achieving 46.1%/63.5% synthesis success rate on the TestP/TestW of the SPoC dataset, respectively." @default.
- W4376606662 created "2023-05-17" @default.
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- W4376606662 date "2023-03-01" @default.
- W4376606662 modified "2023-09-28" @default.
- W4376606662 title "Pseudocode to Code Based on Adaptive Global and Local Information" @default.
- W4376606662 doi "https://doi.org/10.1109/saner56733.2023.00016" @default.
- W4376606662 hasPublicationYear "2023" @default.
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