公开(公告)号：US12229552B2

公开(公告)日：2025-02-18

申请号：US18186458

申请日：2023-03-20

Applicant: Adobe Inc.

Inventor： Charles Robert John Matthews ,  Thomas Stanley Dalton ,  Harinder Singh Sandhu ,  David Alexander Collie ,  Adrian John O'Lenskie

IPC: G06F8/71 ,  G06F8/41

Abstract: In implementations of systems for generating review likelihoods for sets of code, a computing device implements a review system to compile input data based on code data describing information associated with a set of new code to be incorporated into a set of existing code and reviewer data describing information associated with a potential reviewer of sets of code. The review system processes the input data using a machine learning model trained on training data to generate review likelihoods for potential reviewers of sets of code to be selected to review sets of new code. A review likelihood for the potential reviewer of sets of code to be selected to review the set of new code is generated using the machine learning model based on processing the input data. The review system generates an indication of the review likelihood for display in a user interface.

公开(公告)号：US20240319991A1

公开(公告)日：2024-09-26

申请号：US18186458

申请日：2023-03-20

Applicant: Adobe Inc.

Inventor： Charles Robert John Matthews ,  Thomas Stanley Dalton ,  Harinder Singh Sandhu ,  David Alexander Collie ,  Adrian John O'Lenskie

IPC: G06F8/71 ,  G06F8/41

CPC classification number: G06F8/71 ,  G06F8/41

Abstract: In implementations of systems for generating review likelihoods for sets of code, a computing device implements a review system to compile input data based on code data describing information associated with a set of new code to be incorporated into a set of existing code and reviewer data describing information associated with a potential reviewer of sets of code. The review system processes the input data using a machine learning model trained on training data to generate review likelihoods for potential reviewers of sets of code to be selected to review sets of new code. A review likelihood for the potential reviewer of sets of code to be selected to review the set of new code is generated using the machine learning model based on processing the input data. The review system generates an indication of the review likelihood for display in a user interface.