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• W4384155305 abstract "Code representation is a key step in the application of AI in software engineering. Generic NLP representations are effective but do not exploit all the rich structure inherent to code. Recent work has focused on extracting abstract syntax trees (AST) and integrating their structural information into code representations.These AST-enhanced representations advanced the state of the art and accelerated new applications of AI to software engineering. ASTs, however, neglect important aspects of code structure, notably control and data flow, leaving some potentially relevant code signal unexploited. For example, purely image-based representations perform nearly as well as AST-based representations, despite the fact that they must learn to even recognize tokens, let alone their semantics. This result, from prior work, is strong evidence that these new code representations can still be improved; it also raises the question of just what signal image-based approaches are exploiting." @default.
• W4384155305 created "2023-07-14" @default.
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• W4384155305 date "2023-07-12" @default.
• W4384155305 modified "2023-10-16" @default.
• W4384155305 title "CodeGrid: A Grid Representation of Code" @default.
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• W4384155305 doi "https://doi.org/10.1145/3597926.3598141" @default.
• W4384155305 hasPublicationYear "2023" @default.
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