The Emergence of Large Language Models in Static Analysis: A First Look through Micro-Benchmarks
Authors: 
Ashwin Prasad Shivarpatna Venkatesh, Samkutty Sabu, Amir M. Mir, Sofia Reis, Eric BoddenAuthors Info & Claims
Pages 35 - 39
Abstract
The application of Large Language Models (LLMs) in software engineering, particularly in static analysis tasks, represents a paradigm shift in the field. In this paper, we investigate the role that current LLMs can play in improving callgraph analysis and type inference for Python programs. Using the PyCG, HeaderGen, and TypeEvalPy micro-benchmarks, we evaluate 26 LLMs, including OpenAI's GPT series and open-source models such as LLaMA. Our study reveals that LLMs show promising results in type inference, demonstrating higher accuracy than traditional methods, yet they exhibit limitations in callgraph analysis. This contrast emphasizes the need for specialized fine-tuning of LLMs to better suit specific static analysis tasks. Our findings provide a foundation for further research towards integrating LLMs for static analysis tasks.
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Information
Published In
April 2024
140 pages
ISBN:9798400706097
DOI:10.1145/3650105
- Chair:
- David Lo,
- Co-chair:
- Xin Xia,
- Program Chairs:
- Massimiliano Di Penta,
- Xing Hu
This work is licensed under a Creative Commons Attribution International 4.0 License.
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Association for Computing Machinery
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Publication History
Published: 12 June 2024
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- Ministry of Culture and Science of the State of North Rhine-Westphalia
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FORGE '24
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FORGE '24: 2024 IEEE/ACM First International Conference on AI Foundation Models and Software Engineering
April 14, 2024
Lisbon, Portugal
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