Matching graphs generated from source code

A computer program product comprising one or more non-transitory computer-readable storage media having program instructions collectively stored on the one or more non-transitory computer-readable storage media, the program instructions executable to:

• obtain 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;
• determine a mapping across the first abstract syntax tree and the second abstract syntax tree between pairs of matching nodes;
• obtain 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;
• align blocks in the first control-flow graph with blocks in the second control-flow graph based on the mapping across the first abstract syntax tree and the second abstract syntax tree;
• and
• apply 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.

A system comprising:

• a processor, a computer-readable memory, one or more computer-readable storage media, and program instructions collectively stored on the one or more computer-readable storage media, the program instructions executable to:
• obtain 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;
• determine a mapping across the first abstract syntax tree and the second abstract syntax tree between pairs of matching nodes;
• obtain 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;
• align blocks in the first control-flow graph with blocks in the second control-flow graph based on the mapping across the first abstract syntax tree and the second abstract syntax tree;
• and
• apply 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.