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ddiruscio 97fba407ce Major2
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:content {:text "statistical information to initially generate a pool of candidate adversarial examples"},
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:content {:text "We tested ALANCA on eight different models, including both pre-trained models and LLMs in SE."},
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:content {:text "he results of our evaluations clearly demonstrate that ALANCA effectively confuses and neutralizes various neural models with high efficiency"},
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:content {:text "extensive evaluations on four distinct code comprehension tasks: Code Summarization, Method Name Prediction, Code Classification, and Code Clone Detection"},
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:content {:text "roduces a varied collection of candidate adversarial examples, denoted as T , each meticulously crafted to explore various attack strategies"},
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:content {:text " selecting examples to query from the target model and updating the components (Lines 19 to 23) is carefully crafted to capture essential information necessary for conducting effective adversarial attacks"},
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:content {:text "Refactoring involves making small modifications to the source code that preserve the program’s behavior."},
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:content {:text "CODEBERT was built from the pre-trained language model BERT with 125M parameters, while CODE2VEC has an encoder-only model with encoded paths of ASTs as input. "},
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:content {:text "arget model’s prediction with a Siamese network structure"},
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:content {:text "We employ CodeBERT [11] as the underlying framework for the token selector model."},
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:content {:text "We excluded code snippets that lacked function parameters, variable declarations, and methods containing fewer than five lines before conducting our experiments. "},
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:content {:text "three main types of code neural models:"},
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:content {:text " 2.3 million functions with paired documentation from six programming languages was utilized."},
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