公开(公告)号：US12217033B2

公开(公告)日：2025-02-04

申请号：US18475103

申请日：2023-09-26

Applicant: Salesforce, Inc.

Inventor： Yue Wang ,  Weishi Wang ,  Shafiq Rayhan Joty ,  Chu Hong Hoi

IPC: G06F9/44 ,  G06F8/41 ,  G06F18/214 ,  G06F40/20 ,  G06N3/047 ,  G06N3/084

Abstract: Embodiments described herein a code generation and understanding model that builds on a Transformer-based encoder-decoder framework. The code generation and understanding model is configured to derive generic representations for programming language (PL) and natural language (NL) in code domain via pre-training on unlabeled code corpus, and then to benefit many code-related downstream tasks with fine-tuning. Apart from the denoising sequence-to-sequence objectives widely adopted for pre-training on natural language, identifier tagging and prediction pre-training objective is adopted to enable the model to better leverage the crucial token type information from PL, which specifically are the identifiers assigned by developers.

公开(公告)号：US20230376401A1

公开(公告)日：2023-11-23

申请号：US17896873

申请日：2022-08-26

Applicant: Salesforce, Inc.

Inventor： Yue Wang ,  Weishi Wang ,  Shafiq Rayhan Joty ,  Chu Hong Hoi

IPC: G06F11/36 ,  G06F8/65

CPC classification number: G06F11/3624 ,  G06F8/65

Abstract: Systems and methods for automatic program repair using neural network models are described. After a first buggy code patch is received, a first representation of the first buggy code patch is generated using a retriever encoder of a patch retriever. The patch retriever retrieves, based on the first representation, a first bug-fix code pair from a first plurality of bug-fix code pairs. A first augmented buggy code patch is generated based on the first buggy code patch and the first bug-fix code pair. A patch generator generates a fixed code patch based on the first augmented buggy code patch.

公开(公告)号：US20240020102A1

公开(公告)日：2024-01-18

申请号：US18475103

申请日：2023-09-26

Applicant: Salesforce, Inc.

Inventor： Yue Wang ,  Weishi Wang ,  Shafiq Rayhan Joty ,  Chu Hong Hoi

IPC: G06F8/41 ,  G06F40/20 ,  G06N3/084 ,  G06F18/214 ,  G06N3/047

CPC classification number: G06F8/427 ,  G06F40/20 ,  G06N3/084 ,  G06F18/214 ,  G06N3/047

Abstract: Embodiments described herein a code generation and understanding model that builds on a Transformer-based encoder-decoder framework. The code generation and understanding model is configured to derive generic representations for programming language (PL) and natural language (NL) in code domain via pre-training on unlabeled code corpus, and then to benefit many code-related downstream tasks with fine-tuning. Apart from the denoising sequence-to-sequence objectives widely adopted for pre-training on natural language, identifier tagging and prediction pre-training objective is adopted to enable the model to better leverage the crucial token type information from PL, which specifically are the identifiers assigned by developers.