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Dataset . 2021
License: CC BY
Data sources: Datacite
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Dataset . 2021
License: CC BY
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ZENODO
Dataset . 2021
License: CC BY
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ZENODO
Dataset . 2021
License: CC BY
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A Code Token Type Taxonomy-enhanced dataset with pre-computed token types for Python150k

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

A Code Token Type Taxonomy-enhanced dataset with pre-computed token types for Python150k

Abstract

Code Token Type Taxonomy (CT3) is a methodology for refined evaluation of ML-based code completion approaches. We published the CT3-enhanced dataset with pre-computed token types for each token in the Python150k dataset. The dataset was obtained from an empirical study of the below paper: Kim Tuyen Le, Gabriel Rashidi, and Artur Andrzejak. A Methodology for Refined Evaluation of ML-based Code Completion Approaches. In Special Issue on Programming Language Processing, Data Mining and Knowledge Discovery. Please read the README.txt file for detailed information of structuring the enhanced dataset.

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Keywords

code token types, accuracy evaluation, code completion

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
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