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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Bibliographische Detailangaben
Hauptverfasser: Abolhassani, Hassan, Austin, Jacob, Tabachnyk, Maxim, Tarlow, Daniel Stefan, Rasi, Marc Hatcher, Hegna, Jacob Hanson, Johnson, Daniel Dun-ning Woo
Format: Patent
Sprache:eng
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Beschreibung
Zusammenfassung: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.