Neural code completion via re-ranking

A computer-implemented method, comprising:

• analyzing, through a non-machine learning static analysis, a context of a source code program to generate a plurality of unranked candidates to complete a program element of the source code program under development;
• generating a context embedding representing the context and a candidate embedding representing the plurality of unranked candidates;
• computing a probability distribution for the unranked candidates based on the context embedding and a candidate embedding for a respective unranked candidate;
• ranking the plurality of unranked candidates based on a respective probability from the computed probability distribution;
• and
• return k ranked candidates having a highest probability.

A device, comprising:

• at least one processor and a memory;
• wherein the at least one processor is configured to:
• obtain an unordered list of candidates to complete a code snippet in a source code program using a non-neural static analysis based on a context of the code snippet;
• obtain a context embedding of the context and candidate embeddings of the unordered list of candidates;
• rank the unordered list of candidates based on a probability distribution computed from a neural network model, the neural network model configured to generate the probability distribution based on the context embedding of the context and the candidate embeddings of the unordered list of candidates, wherein the context embedding and the candidate embeddings are based on a common token embedding;
• and
• return k ranked candidates having a highest probability.