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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