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An Empirical Study of Large Language Models for Type and Call Graph Analysis, (arXiv2024)
- Abstract: Large Language Models (LLMs) are increasingly being explored for their potential in software engineering, particularly in static analysis tasks. In this study, we investigate the potential of current LLMs to enhance call-graph analysis and type inference for Python and JavaScript programs. We empirically evaluated 24 LLMs, including OpenAI's GPT series and open-source models like LLaMA and Mistral, using existing and newly developed benchmarks. Specifically, we enhanced TypeEvalPy, a micro-bench...
- Labels: static analysis, type inference, call graph analysis
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CALLME: Call Graph Augmentation with Large Language Models for Javascript, (COLM2025)
- Abstract: Building precise call graphs for Javascript programs is a fundamental build-ing block for many important software engineering and security applications such as bug detection, program repair, and refactoring. However, resolving dynamic calls using static analysis is challenging because it requires enumerating all possible values of both the object and the field. As a result, static call graph construction algorithms for Javascript ignore such dynamic calls, resulting in missed edges and a high fa...
- Labels: static analysis, call graph analysis
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LLMs: Understanding Code Syntax and Semantics for Code Analysis, (arXiv2023)
- Abstract: Large language models~(LLMs) demonstrate significant potential to revolutionize software engineering (SE) by exhibiting outstanding performance in SE tasks such as code and document generation. However, the high reliability and risk control requirements in software engineering raise concerns about the lack of interpretability of LLMs. To address this concern, we conducted a study to evaluate the capabilities of LLMs and their limitations for code analysis in SE. We break down the abilities neede...
- Labels: static analysis, data-flow analysis, call graph analysis, data-flow analysis, code model, code model training, source code model, empirical study
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Semantic-Enhanced Indirect Call Analysis with Large Language Models, (ASE2024)
- Abstract: In contemporary software development, the widespread use of indirect calls to achieve dynamic features poses challenges in constructing precise control flow graphs (CFGs), which further impacts the performance of downstream static analysis tasks. To tackle this issue, various types of indirect call analyzers have been proposed. However, they do not fully leverage the semantic information of the program, limiting their effectiveness in real-world scenarios.To address these issues, this paper prop...
- Labels: static analysis, call graph analysis