November 28, 2021 Dataset Open Access

Le, Kim Tuyen; Rashidi, Gabriel; Andrzejak, Artur

DCAT Export

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        <foaf:name>Rashidi, Gabriel</foaf:name>
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    <dct:title>A Code Token Type Taxonomy-enhanced dataset with pre-computed token types for Python150k</dct:title>
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    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2021</dct:issued>
    <dcat:keyword>code completion</dcat:keyword>
    <dcat:keyword>accuracy evaluation</dcat:keyword>
    <dcat:keyword>code token types</dcat:keyword>
    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2021-11-28</dct:issued>
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    <dct:description>&lt;p&gt;Code Token Type Taxonomy (CT3) is a methodology for refined evaluation of ML-based code completion approaches.&lt;/p&gt; &lt;p&gt;We published the&amp;nbsp;CT3-enhanced dataset with pre-computed token types for each token in the &lt;a href="https://www.sri.inf.ethz.ch/py150"&gt;Python150k dataset&lt;/a&gt;.&lt;/p&gt; &lt;p&gt;The dataset was obtained from&amp;nbsp;an empirical study of the below paper:&lt;/p&gt; &lt;p&gt;Kim Tuyen Le, Gabriel Rashidi, and Artur Andrzejak. A Methodology for Refined Evaluation of ML-based Code Completion Approaches. In &lt;em&gt;Special Issue on Programming Language Processing, Data Mining and Knowledge Discovery&lt;/em&gt;.&lt;/p&gt; &lt;p&gt;Please read the README.txt file for detailed information of structuring the enhanced dataset.&lt;/p&gt;</dct:description>
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