Language Models for Code Completion: A Practical Evaluation

Transformer-based language models for automatic code completion have shown great promise so far, yet the evaluation of these models rarely uses real data. This study provides both quantitative and qualitative assessments of three public code language models when completing real-world code. We first...

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Veröffentlicht in:	Proceedings / International Conference on Software Engineering S. 956 - 968
Hauptverfasser:	Izadi, Maliheh, Katzy, Jonathan, van Dam, Tim, Otten, Marc, Popescu, Razvan Mihai, van Deursen, Arie
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	ACM 14.04.2024
Schlagworte:	Analytical models Automatic Code Completion CodeGPT Codes Data models Evaluation IDE InCoder Language Models Open Source Predictive models Training Training data Transformers UniXcoder
ISSN:	1558-1225
Online-Zugang:	Volltext
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