HGVul: A Code Vulnerability Detection Method Based on Heterogeneous Source-Level Intermediate Representation

Zihua Song, Junfeng Wang, Shengli Liu, Zhiyang Fang, Kaiyuan Yang

Security and Communication Networks 2022 article cited by 11

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importance 6/10 ai assisted Software Engineering Research software security stage: research

Problem

Detecting vulnerabilities in open-source source code is hard because existing methods lack fine-grained analysis and perform poorly.

Why it matters

Early vulnerability detection prevents cyber-attack risks in the rapidly growing body of open-source projects.

What the AI did

HGVul extracts a heterogeneous source-level intermediate representation, splits it into semantic subgraphs, and applies a graph neural network with attention to learn code representations.

Results

not stated in source

Impact

The evidence supports a proposed detection method only; no performance figures are stated.

Record

Venue	Security and Communication Networks
Published	2022
Work type	article
Authors	Zihua Song, Junfeng Wang, Shengli Liu, Zhiyang Fang, Kaiyuan Yang
Citations	11 (OpenAlex, at import time)
References	48
Model or method	Graph neural network with attention operations (HGVul)
Organization	not stated in source
Primary source	https://doi.org/10.1155/2022/1919907
Open access	hybrid
Keywords	vulnerability detection, graph neural network, intermediate representation, source code, security

Importance rating

6 / 10

significant — thousands to millions affected

Vulnerability detection prevents cyber-attack risks amid explosive growth of open-source projects; broad security relevance but no quantified scale or impact stated.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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