Real world telemetry shows that the deep learning model provides a better IntelliSense experience and improved productivity for developers in Visual Studio. The user’s editing history to dynamically update the ranking model based on the user’s recent code changes.The specific programming language being used, libraries and frameworks, and other contextual information.In fact, the deep learning model provides better suggestions over time by observing the following context: This is enabled by our machine learning system design, which led to dramatically reduced memory footprint and improved inference speed. All your code stays local – the model runs right on your computer, so there’s no need to transmit code to a remote server for custom model training.Completions tailored to your code – all without the need to train a personal / repository associated team completions model.Provide completions even on unseen libraries – for instance, private user code which is not present in the training set.

This deep learning model ranks candidates provided by static analyzer to provide the following benefits to developers: Since the Visual Studio 2022 release (version 17.0+), custom methods are ranked with the help of a neural encoder model. This model couldn’t rank custom methods that are specific to your code, so we enabled developers to train repository-associated team & personal models to rank custom methods specific to your code in the IntelliSense list.ĭeep learning enables IntelliCode in Visual Studio 2022 to rank custom methods in the IntelliSense list out of the box.

When IntelliCode first shipped in Visual Studio, it used a simple Markov-chain model to rank methods in the IntelliSense list. With original IntelliCode, a custom trained model was previously useful for ranking custom methods in a user’s code. All users of Visual Studio 2022 will receive this improvement automatically.

TL DR – A deep learning model will now help provide starred completions for custom methods in the IntelliSense list.

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