A Combined Approach of Program Analysis and Deep Learning for Code Completion

Abstract

Code completion, a critical feature in integrated development environments, significantly reduces the coding workload for developers. Traditional code completion techniques often focus on the natural language properties of code, overlooking the structural characteristics of programming languages. To address this issue, this paper introduces a novel approach combining program analysis and deep learning to enhance the accuracy and efficiency of code completion. Initially, the research utilizes program analysis techniques to explore the structural and semantic information of code snippets deeply, constructing a program graph. Graph Gated Neural Networks (GGNN) and Transformer technologies are then employed to represent the program graph, capturing the local features and long-range dependencies of the code. The model integrates both the semantic and structural information of code, thereby providing more accurate completion suggestions. Experimental evaluations were conducted on public datasets for Python and JavaScript, two extensively used programming languages. The results demonstrate that our method significantly outperforms existing approaches in terms of code completion accuracy and Mean Reciprocal Rank (MRR).