Generating vector representations of code capturing semantic similarity

A computer program product for obtaining vector representations of code snippets capturing semantic similarity, the computer program product comprising a computer readable storage medium having program code embodied therewith, the program code comprising the programming instructions for:

• collecting a first training set of code snippets which implement a same function representing semantic similarity;
• collecting a second training set of code snippets which implement a different function representing semantic dissimilarity;
• generating a vector representation of a first code snippet and a second code snippet by utilizing a machine learning model, wherein said first and second code snippets are from one of said first and second training sets of code snippets, wherein said machine learning model outputs a first vector and a second vector based on said first and second code snippets;
• generating a loss value utilizing a loss function that is proportional to a distance between said first and second vectors in response to receiving said first and second code snippets from said first training set of code snippets;
• generating a loss value utilizing said loss function that is inverse to said distance between said first and second vectors in response to receiving said first and second code snippets from said second training set of code snippets;
• and
• training said machine learning model to generate vector representations of code snippets capturing semantic similarity between said code snippets by minimizing said loss value.

A system, comprising:

• a memory for storing a computer program for obtaining vector representations of code snippets capturing semantic similarity;
• and
• a processor connected to said memory, wherein said processor is configured to execute the program instructions of the computer program comprising:
• collecting a first training set of code snippets which implement a same function representing semantic similarity;
• collecting a second training set of code snippets which implement a different function representing semantic dissimilarity;
• generating a vector representation of a first code snippet and a second code snippet by utilizing a machine learning model, wherein said first and second code snippets are from one of said first and second training sets of code snippets, wherein said machine learning model outputs a first vector and a second vector based on said first and second code snippets;
• generating a loss value utilizing a loss function that is proportional to a distance between said first and second vectors in response to receiving said first and second code snippets from said first training set of code snippets;
• generating a loss value utilizing said loss function that is inverse to said distance between said first and second vectors in response to receiving said first and second code snippets from said second training set of code snippets;
• and
• training said machine learning model to generate vector representations of code snippets capturing semantic similarity between said code snippets by minimizing said loss value.