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Effectiveness of ChatGPT for Static Analysis: How Far Are We?

Authors:

Mohammad Mahdi Mohajer,

Nima Shiri Harzevili,

Alvine Boaye Belle,

Hung Viet Pham,

Song WangAuthors Info & Claims

AIware 2024: Proceedings of the 1st ACM International Conference on AI-Powered Software

Pages 151 - 160

https://doi.org/10.1145/3664646.3664777

Published: 10 July 2024 Publication History

Abstract

This paper conducted a novel study to explore the capabilities of ChatGPT, a state-of-the-art LLM, in static analysis tasks such as static bug detection and false positive warning removal. In our evaluation, we focused on two types of typical and critical bugs targeted by static bug detection, i.e., Null Dereference and Resource Leak, as our subjects. We employ Infer, a well-established static analyzer, to aid the gathering of these two bug types from 10 open-source projects. Consequently, our experiment dataset contains 222 instances of Null Dereference bugs and 46 instances of Resource Leak bugs. Our study demonstrates that ChatGPT can achieve remarkable performance in the mentioned static analysis tasks, including bug detection and false-positive warning removal. In static bug detection, ChatGPT achieves accuracy and precision values of up to 68.37% and 63.76% for detecting Null Dereference bugs and 76.95% and 82.73% for detecting Resource Leak bugs, improving the precision of the current leading bug detector, Infer by 12.86% and 43.13% respectively. For removing false-positive warnings, ChatGPT can reach a precision of up to 93.88% for Null Dereference bugs and 63.33% for Resource Leak bugs, surpassing existing state-of-the-art false-positive warning removal tools.

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Cited By

Odu OBelle AWang SKpodjedo SLethbridge THemmati H(2025)Automatic instantiation of assurance cases from patterns using large language modelsJournal of Systems and Software10.1016/j.jss.2025.112353222(112353)Online publication date: Apr-2025
https://doi.org/10.1016/j.jss.2025.112353
Ezenwoye OPinconschi ERoberts E(2024)Exploring AI for Vulnerability Detection and Repair2024 Cyber Awareness and Research Symposium (CARS)10.1109/CARS61786.2024.10778769(1-9)Online publication date: 28-Oct-2024
https://doi.org/10.1109/CARS61786.2024.10778769

Index Terms

Effectiveness of ChatGPT for Static Analysis: How Far Are We?
1. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Software defect analysis
        Software testing and debugging
  2. Software organization and properties
    1. Software functional properties
      1. Formal methods
        Automated static analysis

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cover image ACM Conferences

AIware 2024: Proceedings of the 1st ACM International Conference on AI-Powered Software

July 2024

182 pages

ISBN:9798400706851

DOI:10.1145/3664646

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Bram Adams
Queen’s University, Canada
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Thomas Zimmermann
Microsoft Research, USA
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Ipek Ozkaya
Carnegie Mellon University, USA
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Dayi Lin
Huawei, Canada
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Jie M. Zhang
King’s College London, United Kingdom

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Cited By

Odu OBelle AWang SKpodjedo SLethbridge THemmati H(2025)Automatic instantiation of assurance cases from patterns using large language modelsJournal of Systems and Software10.1016/j.jss.2025.112353222(112353)Online publication date: Apr-2025
https://doi.org/10.1016/j.jss.2025.112353
Ezenwoye OPinconschi ERoberts E(2024)Exploring AI for Vulnerability Detection and Repair2024 Cyber Awareness and Research Symposium (CARS)10.1109/CARS61786.2024.10778769(1-9)Online publication date: 28-Oct-2024
https://doi.org/10.1109/CARS61786.2024.10778769

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