公开(公告)号：US20240231765A1

公开(公告)日：2024-07-11

申请号：US18618371

申请日：2024-03-27

Applicant: Google LLC

Inventor： Daniel Dun-ning Woo Johnson ,  Daniel Stefan Tarlow ,  Maxim Tabachnyk ,  Marc Hatcher Rasi ,  Jacob Austin ,  Hassan Abolhassani ,  Jacob Hanson Hegna

IPC: G06F8/33

CPC classification number: G06F8/33

Abstract: 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.

公开(公告)号：US11972234B2

公开(公告)日：2024-04-30

申请号：US17832199

申请日：2022-06-03

Applicant: Google LLC

Inventor： Daniel Dun-Ning Woo Johnson ,  Daniel Stefan Tarlow ,  Maxim Tabachnyk ,  Marc Hatcher Rasi ,  Jacob Austin ,  Hassan Abolhassani ,  Jacob Hanson Hegna

IPC: G06F8/33

CPC classification number: G06F8/33

Abstract: 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.

公开(公告)号：US20230393817A1

公开(公告)日：2023-12-07

申请号：US17832199

申请日：2022-06-03

Applicant: Google LLC

Inventor： Daniel Dun-ning Woo Johnson ,  Daniel Stefan Tarlow ,  Maxim Tabachnyk ,  Marc Hatcher Rasi ,  Jacob Austin ,  Hassan Abolhassani ,  Jacob Hanson Hegna

IPC: G06F8/33

CPC classification number: G06F8/33

Abstract: 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.