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- W4386005320 abstract "It is a common mistake to neglect high-level constructs while programming, often made by programmers new to Python. Such bad practice leads to verbose and nonidiomatic code, which generally also runs slower. Our goal is to refactor code containing such low-level snippets while preserving the original behaviour of the program. We present a method to localize and idiomatize nonidiomatic parts in Python source code. Localization is formulated as a sequence tagging task, where each line is either part of a low-level snippet or not. The task is solved by a recurrent neural network. Generating the idiomatic counterparts from nonidiomatic code is tackled by a sequence-to-sequence model with attention. The method was evaluated on a real-world dataset containing programs written by students, and it outperformed previous work with higher precision and F1-score." @default.
- W4386005320 created "2023-08-20" @default.
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- W4386005320 date "2023-01-01" @default.
- W4386005320 modified "2023-10-17" @default.
- W4386005320 title "Localizing and Idiomatizing Nonidiomatic Python Code with Deep Learning" @default.
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- W4386005320 doi "https://doi.org/10.1007/978-3-031-37963-5_47" @default.
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