Generating and using joint representations of source code

    公开(公告)号:US11169786B2

    公开(公告)日:2021-11-09

    申请号:US16781344

    申请日:2020-02-04

    Abstract: Implementations are described herein for generating embeddings of source code using both the language and graph domains, and leveraging combinations of these semantically-rich and structurally-informative embeddings for various purposes. In various implementations, tokens of a source code snippet may be applied as input across a sequence-processing machine learning model to generate a plurality of token embeddings. A graph may also be generated based on the source code snippet. A joint representation may be generated based on the graph and the incorporated token embeddings. The joint representation generated from the source code snippet may be compared to one or more other joint representations generated from one or more other source code snippets to make a determination about the source code snippet.

    GENERATING AND USING JOINT REPRESENTATIONS OF SOURCE CODE

    公开(公告)号:US20210240453A1

    公开(公告)日:2021-08-05

    申请号:US16781344

    申请日:2020-02-04

    Abstract: Implementations are described herein for generating embeddings of source code using both the language and graph domains, and leveraging combinations of these semantically-rich and structurally-informative embeddings for various purposes. In various implementations, tokens of a source code snippet may be applied as input across a sequence-processing machine learning model to generate a plurality of token embeddings. A graph may also be generated based on the source code snippet. A joint representation may be generated based on the graph and the incorporated token embeddings. The joint representation generated from the source code snippet may be compared to one or more other joint representations generated from one or more other source code snippets to make a determination about the source code snippet.

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