Multi-View Graph Representation for Programming Language Processing: An Investigation into Algorithm Detection

TL;DR

This paper introduces a multi-view graph representation for source code that captures both data and control flow, enhancing program understanding and detection of algorithms.

Contribution

It proposes a novel multi-view graph approach combining data and control flow with GNNs, improving program representation over existing syntax-focused methods.

Findings

MVG outperforms previous methods on algorithm detection tasks

The approach effectively captures multiple aspects of program semantics

Experimental results on POJ-104 and ALG-109 datasets demonstrate strong performance

Abstract

Program representation, which aims at converting program source code into vectors with automatically extracted features, is a fundamental problem in programming language processing (PLP). Recent work tries to represent programs with neural networks based on source code structures. However, such methods often focus on the syntax and consider only one single perspective of programs, limiting the representation power of models. This paper proposes a multi-view graph (MVG) program representation method. MVG pays more attention to code semantics and simultaneously includes both data flow and control flow as multiple views. These views are then combined and processed by a graph neural network (GNN) to obtain a comprehensive program representation that covers various aspects. We thoroughly evaluate our proposed MVG approach in the context of algorithm detection, an important and challenging subfield of PLP. Specifically, we use a public dataset POJ-104 and also construct a new challenging dataset ALG-109 to test our method. In experiments, MVG outperforms previous methods significantly, demonstrating our model's strong capability of representing source code.