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logseq/assets/ase2022-paper108_1661352547766_0.edn
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2025-06-05 22:07:12 +02:00

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:content {:text "predicting code from natural language descriptions of the desired functionality"},
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:content {:text "e find at least 21 papers published since 2015 that use BLEU as a metric to evaluate code prediction"},
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:content {:text "The basic idea of BLEU is to compare a prediction against one or more reference solutions by computing the overlap between n-grams, i.e., contiguous sequences of code tokens"},
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:content {:text "trivially shared n-grams, i.e., n-grams that occur across code written in the same language without implying any deeper relationship or semantic similarity. "},
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:content {:text "This paper presents CrystalBLEU, a metric to precisely and efficiently evaluate code similarity despite trivially shared n-grams."},
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:content {:text "distinguishability measures how much more similar code examples known to be semantically equivalent are compared to code examples that are not equivalent to each other."},
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:content {:text "Concode"},
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:content {:text "BigCloneBench"},
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:content {:text "it is more effective at distinguishing semantically similar code from code merely written in the same language."},
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:content {:text "most common n-grams in programming languages appear relatively more often than the most common n-grams in natural languages, and that they appear in many different programs regardless of their semantic similarity."},
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