Semantic similarity-based program retrieval: a multi-relational graph perspective

In this paper, we formulate the program retrieval problem as a graph similarity problem. This is achieved by first explicitly representing queries and program snippets as AMR and CPG, respectively. Then, through intra-level and inter-level attention mechanisms to infer fine-grained correspondence by propagating node correspondence along the graph edge. Moreover, such a design can learn correspondence of nodes at different levels, which were mostly ignored by previous works. Experiments have demonstrated the superiority of USRAE.