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Fast and Memory-Efficient Neural Code Completion

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arxiv 2004.13651 v4 pith:R7EMJL4J submitted 2020-04-28 cs.SE cs.LG

Alexey Svyatkovskiy , Sebastian Lee , Anna Hadjitofi , Maik Riechert , Juliana Franco , Miltiadis Allamanis This is my paper · ORCID
classification cs.SEcs.LG
keywords completionneuralcodedesigndevelopmentframeworkmodelmodels
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Code completion is one of the most widely used features of modern integrated development environments (IDEs). While deep learning has made significant progress in the statistical prediction of source code, state-of-the-art neural network models consume hundreds of megabytes of memory, bloating the development environment. We address this in two steps: first we present a modular neural framework for code completion. This allows us to explore the design space and evaluate different techniques. Second, within this framework we design a novel reranking neural completion model that combines static analysis with granular token encodings. The best neural reranking model consumes just 6 MB of RAM, - 19x less than previous models - computes a single completion in 8 ms, and achieves 90% accuracy in its top five suggestions.

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Cited by 4 Pith papers

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