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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| Vydané v: | Proceedings of ACM on programming languages Ročník 8; číslo OOPSLA1; s. 474 - 499 |
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New York, NY, USA
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29.04.2024
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| Abstract | 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. |
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| AbstractList | 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 analysis with LLMs, setting a compelling direction for future research in this area. 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. |
| ArticleNumber | 111 |
| Author | Qian, Zhiyun Hao, Yu Li, Haonan Zhai, Yizhuo |
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| Cites_doi | 10.1145/3319535.3354244 10.1109/SP46215.2023.10179420 10.48550/arXiv.2205.00445 10.1145/3582688 10.5281/zenodo.10780591 10.1145/3464457 10.1145/3520312.3534862 10.1109/ICSE48619.2023.00085 10.1145/3571730 10.48550/arXiv.2302.04761 10.48550/arXiv.2306.01987 10.1017/CBO9780511546990 10.48550/arXiv.2205.12255 10.1145/3368089.3409686 10.5555/3618408.3619552 10.1145/2892208.2892235 10.1145/3597503.3639117 |
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| Title | Enhancing Static Analysis for Practical Bug Detection: An LLM-Integrated Approach |
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