Enhancing Static Analysis for Practical Bug Detection: An LLM-Integrated Approach

While static analysis is instrumental in uncovering software bugs, its precision in analyzing large and intricate codebases remains challenging. The emerging prowess of Large Language Models (LLMs) offers a promising avenue to address these complexities. In this paper, we present LLift, a pioneering...

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Bibliographic Details
Published in:Proceedings of ACM on programming languages Vol. 8; no. OOPSLA1; pp. 474 - 499
Main Authors: Li, Haonan, Hao, Yu, Zhai, Yizhuo, Qian, Zhiyun
Format: Journal Article
Language:English
Published: New York, NY, USA ACM 29.04.2024
Subjects:
Automated static analysis
Computing methodologies
Natural language processing
Security and privacy
Software and its engineering
Systems security
large language model
Static analysis
bug detection
ISSN:2475-1421, 2475-1421
Online Access:Get full text
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Summary:While static analysis is instrumental in uncovering software bugs, its precision in analyzing large and intricate codebases remains challenging. The emerging prowess of Large Language Models (LLMs) offers a promising avenue to address these complexities. In this paper, we present LLift, a pioneering framework that synergizes static analysis and LLMs, with a spotlight on identifying Use Before Initialization (UBI) bugs within the Linux kernel. Drawing from our insights into variable usage conventions in Linux, we enhance path analysis using post-constraint guidance. This approach, combined with our methodically crafted procedures, empowers LLift to adeptly handle the challenges of bug-specific modeling, extensive codebases, and the unpredictable nature of LLMs. Our real-world evaluations identified four previously undiscovered UBI bugs in the mainstream Linux kernel, which the Linux community has acknowledged. This study reaffirms the potential of marrying static program analysis with LLMs, setting a compelling direction for future research in this area.
ISSN:2475-1421
2475-1421
DOI:10.1145/3649828