CodeFill: Multi-token Code Completion by Jointly Learning from Structure and Naming Sequences

• URL: http://arxiv.org/abs/2202.06689v1
• Date: Mon, 14 Feb 2022 13:26:54 GMT
• Title: CodeFill: Multi-token Code Completion by Jointly Learning from Structure and Naming Sequences
• Authors: Maliheh Izadi, Roberta Gismondi, Georgios Gousios
• Abstract summary: We present CodeFill, a language model for autocompletion that combines learned structure and naming information. CodeFill is trained both for single-token and multi-token (statement) prediction. To make the evaluation more realistic, we develop a method to automatically infer points in the source code at which completion matters.
• Score: 7.661675959139121
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Code completion is an essential feature of IDEs, yet current autocompleters are restricted to either grammar-based or NLP-based single token completions. Both approaches have significant drawbacks: grammar-based autocompletion is restricted in dynamically-typed language environments, whereas NLP-based autocompleters struggle to understand the semantics of the programming language and the developer's code context. In this work, we present CodeFill, a language model for autocompletion that combines learned structure and naming information. Using a parallel Transformer architecture and multi-task learning, CodeFill consumes sequences of source code token names and their equivalent AST token types. Uniquely, CodeFill is trained both for single-token and multi-token (statement) prediction, which enables it to learn long-range dependencies among grammatical and naming elements. We train CodeFill on two datasets, consisting of 29M and 425M lines of code, respectively. To make the evaluation more realistic, we develop a method to automatically infer points in the source code at which completion matters. We compare CodeFill against four baselines and two state-of-the-art models, GPT-C and TravTrans+.CodeFill surpasses all baselines in single token prediction (MRR: 70.9% vs. 66.2% and 67.8%) and outperforms the state of the art for multi-token prediction (ROUGE-L: 63.7% vs. 52.4% and 59.2%, for n=4 tokens). We publicly release our source code and datasets.

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