Machine-learned models for generating code snippets with predicted placeholders for optimizing software development

Systems and methods of the present disclosure are directed to a method for machine-learned code segment prediction for optimizing software development. The method includes obtaining an incomplete segment of code. The method includes processing the incomplete segment of code with a machine-learned co...

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Bibliographic Details
Main Authors Abolhassani, Hassan, Austin, Jacob, Tabachnyk, Maxim, Johnson, Daniel Dun-Ning Woo, Tarlow, Daniel Stefan, Rasi, Marc Hatcher, Hegna, Jacob Hanson
Format Patent
LanguageEnglish
Published 30.04.2024
Subjects
CALCULATING
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
PHYSICS
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Summary:Systems and methods of the present disclosure are directed to a method for machine-learned code segment prediction for optimizing software development. The method includes obtaining an incomplete segment of code. The method includes processing the incomplete segment of code with a machine-learned code prediction model to obtain a sampled set of segment completion predictions that include code that completes the incomplete segment of code. The method includes determining an aggregated segment completion prediction from the sampled set of segment completion predictions. The method includes replacing a portion of the aggregated segment completion prediction with an input field, wherein the portion of the aggregated segment completion prediction is associated with a degree of certainty less than a threshold degree of certainty.
Bibliography:Application Number: US202217832199