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Despite their ability to aid developers in detecting potential defects earlyin the software development life cycle, static analysis tools often suffer fromprecision issues (i.e., high false positive rates of reported alarms). Toimprove the availability of these tools, many automated warning identificationtechniques have been proposed to assist developers in classifying falsepositive alarms. However, existing approaches mainly focus on usinghand-engineered features or statement-level abstract syntax tree tokensequences to represent the defective code, failing to capture semantics fromthe reported alarms. To overcome the limitations of traditional approaches,this paper employs deep neural networks' powerful feature extraction andrepresentation abilities to generate code semantics from control flow graphpaths for warning identification. The control flow graph abstractly representsthe execution process of a given program. Thus, the generated path sequences ofthe control flow graph can guide the deep neural networks to learn semanticinformation about the potential defect more accurately. In this paper, wefine-tune the pre-trained language model to encode the path sequences andcapture the semantic representations for model building. Finally, this paperconducts extensive experiments on eight open-source projects to verify theeffectiveness of the proposed approach by comparing it with thestate-of-the-art baselines.

Automated Static Warning Identification via Path-based Semantic Representation