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In order to ensure the quality of software and prevent attacks from hackerson critical systems, static analysis tools are frequently utilized to detectvulnerabilities in the early development phase. However, these tools oftenreport a large number of warnings with a high false-positive rate, which causesmany difficulties for developers. In this paper, we introduce VulRG, a novelapproach to address this problem. Specifically, VulRG predicts and ranks thewarnings based on their likelihoods to be true positive. To predict thatlikelihood, VulRG combines two deep learning models CNN and BiGRU to capturethe context of each warning in terms of program syntax, control flow, andprogram dependence. Our experimental results on a real-world dataset of 6,620warnings show that VulRG's Recall at Top-50% is 90.9%. This means that usingVulRG, 90% of the vulnerabilities can be found by examining only 50% of thewarnings. Moreover, at Top-5%, VulRG can improve the state-of-the-art approachby +30% in both Precision and Recall.

Using Multiple Code Representations to Prioritize Static Analysis Warnings