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US Patent 12008347 Matching graphs generated from source code

Patent 12008347 was granted and assigned to Google on June, 2024 by the United States Patent and Trademark Office.

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Contents

Is a
Patent
Patent
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Patent attributes

Patent Applicant
Google
Google
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Current Assignee
Google
Google
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
120083470
Patent Inventor Names
Qianyu Zhang0
Date of Patent
June 11, 2024
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Patent Application Number
179408310
Date Filed
September 8, 2022
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Patent Citations
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US Patent 10983761 Deep learning enhanced code completion system
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US Patent 8296744 Tree-based directed graph programming structures for a declarative programming language
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US Patent 9223554 Recovering source code structure from program binaries
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US Patent 10303448 Systems and methods for graph-based analysis of software
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US Patent 10866885 Programmatically generating software test libraries for functional safety
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US Patent 10871950 Persistent annotation of syntax graphs for code optimization
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Patent Primary Examiner
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Satish Rampuria
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CPC Code
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G06F 8/433
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G06N 3/02
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G06N 20/00
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G06F 8/427
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Patent abstract

Techniques are described herein for training a machine learning model and using the trained machine learning model to more accurately determine alignments between matching/corresponding nodes of predecessor and successor graphs representing predecessor and successor source code snippets. A method includes: obtaining a first abstract syntax tree that represents a predecessor source code snippet and a second abstract syntax tree that represents a successor source code snippet; determining a mapping across the first and second abstract syntax trees; obtaining a first control-flow graph that represents the predecessor source code snippet and a second control-flow graph that represents the successor source code snippet; aligning blocks in the first control-flow graph with blocks in the second control-flow graph; and applying the aligned blocks as inputs across a trained machine learning model to generate an alignment of nodes in the first abstract syntax tree with nodes in the second abstract syntax tree.

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