US 11,928,156 B2
Learning-based automated machine learning code annotation with graph neural network
Dakuo Wang, Cambridge, MA (US); Lingfei Wu, Elmsford, NY (US); Xuye Liu, Troy, NY (US); Yi Wang, Ann Arbor, MI (US); Chuang Gan, Cambridge, MA (US); Jing Xu, Xi'an (CN); Xue Ying Zhang, Xi'an (CN); Jun Wang, Xi'an (CN); and Jing James Xu, Xi'an (CN)
Assigned to International Business Machines Corporation, Armonk, NY (US)
Filed by International Business Machines Corporation, Armonk, NY (US)
Filed on Nov. 3, 2020, as Appl. No. 17/088,018.
Prior Publication US 2022/0138266 A1, May 5, 2022
Int. Cl. G06N 3/08 (2023.01); G06F 16/901 (2019.01); G06F 16/9032 (2019.01); G06F 16/955 (2019.01); G06F 40/211 (2020.01)
CPC G06F 16/90332 (2019.01) [G06F 16/9024 (2019.01); G06F 16/9558 (2019.01); G06F 40/211 (2020.01); G06N 3/08 (2013.01)] 15 Claims
OG exemplary drawing
 
1. A method comprising:
obtaining, at a computing device, a segment of computer code;
with a classification module of a machine learning system executing on said computing device, determining a stage from a predefined set of stages of a data science workflow to which said segment of computer code corresponds and determining a required annotation category from a predefined set of categories for said segment of computer code based on said stage of said data science workflow;
with an annotation generation module of said machine learning system executing on said computing device, generating a natural language annotation of said segment of computer code based on said segment of computer code and said required annotation category;
generating an Abstract Syntax Tree (AST) structure of at least said segment of computer code, wherein said natural language annotation of said segment of computer code is further based on said Abstract Syntax Tree (AST) structure, wherein said annotation generation module includes a graph neural network; and
providing said natural language annotation to a user interface for display adjacent said segment of computer code.