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Identifying vulnerable code is a precautionary measure to counter softwaresecurity breaches. Tedious expert effort has been spent to build staticanalyzers, yet insecure patterns are barely fully enumerated. This workexplores a deep learning approach to automatically learn the insecure patternsfrom code corpora. Because code naturally admits graph structures with parsing,we develop a novel graph neural network (GNN) to exploit both the semanticcontext and structural regularity of a program, in order to improve predictionperformance. Compared with a generic GNN, our enhancements include a synthesisof multiple representations learned from the several parsed graphs of aprogram, and a new training loss metric that leverages the fine granularity oflabeling. Our model outperforms multiple text, image and graph-basedapproaches, across two real-world datasets.

Software Vulnerability Detection via Deep Learning over Disaggregated Code Graph Representation