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- W3132455461 abstract "This paper presents Deepsy, a Natural Language-based synthesizer to assist source code analysis. It takes English descriptions of to-be-found code patterns as its inputs, and automatically produces ASTMatcher expressions that are directly usable by LLVM/Clang to materialize intended code analysis. The code analysis domain features profuse complexities in data types and operations, which make it elusive for prior rule-based synthesizers to tackle. On the other hand, machine learning-based solutions are neither applicable due to the scarcity of well labeled examples. This paper presents how Deepsy addresses the challenges by leveraging deep Natural Language Processing (NLP) and creating a new technique named dependency tree-based co-evolvement.Deepsy features an effective design that seamlessly integrates Natural Language dependency analysis into code analysis and meanwhile synergizes it with type-based narrowing and domain-specific guidance. Deepsy achieves over 70.0% expression-level accuracy and 85.1% individual API-level accuracy, significantly outperforming previous solutions." @default.
- W3132455461 created "2021-03-01" @default.
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- W3132455461 date "2021-02-27" @default.
- W3132455461 modified "2023-10-16" @default.
- W3132455461 title "Deep NLP-based co-evolvement for synthesizing code analysis from natural language" @default.
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- W3132455461 doi "https://doi.org/10.1145/3446804.3446852" @default.
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