Representing source code in vector space to detect errors

A computer program product for representing source code in vector space, the computer program product comprising a computer readable storage medium having program code embodied therewith, the program code comprising the programming instructions for:

• parsing source code into an abstract syntax tree;
• traversing said abstract syntax tree to produce a sequence of tokens;
• constructing token embeddings for a subset of said sequence of tokens;
• inputting said token embeddings into an encoder artificial neural network for encoding said token embeddings;
• initializing a decoder artificial neural network with a final internal cell state of said encoder artificial neural network when encoding said token embeddings;
• running said decoder artificial neural network a same number of steps as encoding performed by said encoder artificial neural network;
• using said final internal cell state of said encoder artificial neural network as a code representation vector in response to completing said running of said decoder artificial neural network;
• and
• using said code representation vector to detect errors in said source code.

A system, comprising:

• a memory for storing a computer program for representing source code in vector space;
• and
• a processor connected to said memory, wherein said processor is configured to execute the program instructions of the computer program comprising:
• parsing source code into an abstract syntax tree;
• traversing said abstract syntax tree to produce a sequence of tokens;
• constructing token embeddings for a subset of said sequence of tokens;
• inputting said token embeddings into an encoder artificial neural network for encoding said token embeddings;
• initializing a decoder artificial neural network with a final internal cell state of said encoder artificial neural network when encoding said token embeddings;
• running said decoder artificial neural network a same number of steps as encoding performed by said encoder artificial neural network;
• using said final internal cell state of said encoder artificial neural network as a code representation vector in response to completing said running of said decoder artificial neural network;
• and
• using said code representation vector to detect errors in said source code.