发明授权
- 专利标题: Machine learning-based program analysis using synthetically generated labeled data
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申请号: US16699378申请日: 2019-11-29
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公开(公告)号: US11593675B1公开(公告)日: 2023-02-28
- 发明人: Pranav Garg , Srinivasan Sengamedu Hanumantha Rao
- 申请人: Amazon Technologies, Inc.
- 申请人地址: US WA Seattle
- 专利权人: Amazon Technologies, Inc.
- 当前专利权人: Amazon Technologies, Inc.
- 当前专利权人地址: US WA Seattle
- 代理机构: Nicholson De Vos Webster & Elliott LLP
- 主分类号: G06F21/57
- IPC分类号: G06F21/57 ; G06N20/00 ; G06N5/04 ; G06F8/75
摘要:
Techniques for performing machine learning-based program analysis using synthetically generated labeled data are described. A method of performing machine learning-based program analysis using synthetically generated labeled data may include receiving a request to perform program analysis on code, determining a first portion of the code associated with a first error type, sending the first portion of the code to an endpoint of a machine learning service associated with an error detection model to detect the first error type, the error detection model trained using synthetically generated labeled data, and receiving inference results from the error detection model identifying one or more errors of the first error type in the first portion of the code.
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