DeepSim: deep learning code functional similarity 论文

摘要

Measuring code similarity is fundamental for many software engineering tasks, e.g., code search, refactoring and reuse. However, most existing techniques focus on code syntactical similarity only, while measuring code functional similarity remains a challenging problem. In this paper, we propose a novel approach that encodes code control flow and data flow into a semantic matrix in which each element is a high dimensional sparse binary feature vector, and we design a new deep learning model that measures code functional similarity based on this representation. By concatenating hidden representations learned from a code pair, this new model transforms the problem of detecting functionally similar code to binary classification, which can effectively learn patterns between functionally similar code with very different syntactics.