Contextuality of Code Representation Learning

Advanced machine learning models (ML) have been successfully leveraged in several software engineering (SE) applications. The existing SE techniques have used the embedding models ranging from static to contextualized ones to build the vectors for program units. The contextualized vectors address a phenomenon in natural language texts called polysemy, which is the coexistence of different meanings of a word/phrase. However, due to different nature, program units exhibit the nature of mixed polysemy. Some code tokens and statements exhibit polysemy while other tokens (e.g., keywords, separators, and operators) and statements maintain the same meaning in different contexts. A natural question is whether static or contextualized embeddings fit better with the nature of mixed polysemy in source code. The answer to this question is helpful for the SE researchers in selecting the right embedding model. We conducted experiments on 12 popular sequence-/tree-/graph-based embedding models and on the samples of a dataset of 10,222 Java projects with +14M methods. We present several contextuality evaluation metrics adapted from natural-language texts to code structures to evaluate the embeddings from those models. Among several findings, we found that the models with higher contextuality help a bug detection model perform better than the static ones. Neither static nor contextualized embedding models fit well with the mixed polysemy nature of source code. Thus, we develop HYCODE, a hybrid embedding model that fits better with the nature of mixed polysemy in source code.