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An Empirical Study on the Usage of Transformer Models for Code Completion
[article]
2021
arXiv
pre-print
Code completion aims at speeding up code writing by predicting the next code token(s) the developer is likely to write. Works in this field focused on improving the accuracy of the generated predictions, with substantial leaps forward made possible by deep learning (DL) models. However, code completion techniques are mostly evaluated in the scenario of predicting the next token to type, with few exceptions pushing the boundaries to the prediction of an entire code statement. Thus, little is
arXiv:2108.01585v1
fatcat:le2gets3rzctxd5wlobbiooium