Search

Static Application Security Testing(SAST) tools are crucial for early bug detection and code quality but often generate false positives that slow development. Automating false positive mitigation is thus essential for advancing SAST tools. Past efforts use static/dynamic analysis or machine learning. The advent of Large Language Models, adept at understanding natural language and code, offers promising ways to improve the accuracy and usability of SAST tools. However, existing LLM-based methods need improvement in two key areas: first, extracted code snippets related to warnings are often cluttered with irrelevant control and data flows, reducing precision; second, critical code contexts are often missing, leading to incomplete representations that can mislead LLMs and cause inaccurate assessments. To ensure the use of precise and complete code context, thereby avoiding misguidance and enabling LLMs to reach accurate conclusions, we propose LLM4FPM. One of its core components is eCPG-Slicer, which builds an extended code property graph and extracts line-level, precise code context. Moreover, LLM4FPM incorporates FARF algorithm, which builds a file reference graph and then efficiently detects all files related to a warning in linear time, enabling eCPG-Slicer to gather complete code context across these files. We evaluate LLM4FPM on Juliet dataset, where it comprehensively outperforms the baseline, achieving an F1 score above 99% across various CWEs. LLM4FPM leverages a free, open-source model, avoiding costly alternatives and reducing inspection costs by up to $2758 per run on Juliet, with an average inspection time of 4.7 seconds per warning. Our work emphasizes the critical impact of precise and complete code context and highlights the potential of combining program analysis with LLMs, improving the quality and efficiency of software development., Comment: 21 pages