The Emergence of Large Language Models in Static Analysis: A First Look through Micro-BenchmarksNew Idea Paper
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.
Sun 14 AprDisplayed time zone: Lisbon change
11:00 - 12:30 | Foundation Models for Software Quality AssuranceResearch Track at Luis de Freitas Branco Chair(s): Matteo Ciniselli Università della Svizzera Italiana | ||
11:00 14mFull-paper | Deep Multiple Assertions GenerationFull Paper Research Track | ||
11:14 14mFull-paper | MeTMaP: Metamorphic Testing for Detecting False Vector Matching Problems in LLM Augmented GenerationFull Paper Research Track Guanyu Wang Beijing University of Posts and Telecommunications, Yuekang Li The University of New South Wales, Yi Liu Nanyang Technological University, Gelei Deng Nanyang Technological University, Li Tianlin Nanyang Technological University, Guosheng Xu Beijing University of Posts and Telecommunications, Yang Liu Nanyang Technological University, Haoyu Wang Huazhong University of Science and Technology, Kailong Wang Huazhong University of Science and Technology | ||
11:28 14mFull-paper | Planning to Guide LLM for Code Coverage PredictionFull Paper Research Track Hridya Dhulipala University of Texas at Dallas, Aashish Yadavally University of Texas at Dallas, Tien N. Nguyen University of Texas at Dallas | ||
11:42 7mShort-paper | The Emergence of Large Language Models in Static Analysis: A First Look through Micro-BenchmarksNew Idea Paper Research Track Ashwin Prasad Shivarpatna Venkatesh University of Paderborn, Samkutty Sabu University of Paderborn, Amir Mir Delft University of Technology, Sofia Reis Instituto Superior Técnico, U. Lisboa & INESC-ID, Eric Bodden | ||
11:49 14mFull-paper | Reality Bites: Assessing the Realism of Driving Scenarios with Large Language ModelsFull Paper Research Track Jiahui Wu Simula Research Laboratory and University of Oslo, Chengjie Lu Simula Research Laboratory and University of Oslo, Aitor Arrieta Mondragon University, Tao Yue Beihang University, Shaukat Ali Simula Research Laboratory and Oslo Metropolitan University | ||
12:03 7mShort-paper | Assessing the Impact of GPT-4 Turbo in Generating Defeaters for Assurance CasesNew Idea Paper Research Track Kimya Khakzad Shahandashti York University, Mithila Sivakumar York University, Mohammad Mahdi Mohajer York University, Alvine Boaye Belle York University, Song Wang York University, Timothy Lethbridge University of Ottawa | ||
12:10 20mOther | Discussion Research Track | ||