CoT prompting improves LLM performance on control-flow deobfuscation of C benchmarks, yielding ~16% better CFG reconstruction and ~20.5% better semantic preservation for GPT5 versus zero-shot prompting.
Static code analysis in the AI era: An in-depth exploration of SEPTEMBER 2024 52 the concept, function, and potential of intelligent code analysis agents.CoRR, abs/2310.08837
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A literature survey that collects and categorizes 124 papers on LLM-based agents for software engineering from SE and agent perspectives.
citing papers explorer
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Analyzing Chain of Thought (CoT) Approaches in Control Flow Code Deobfuscation Tasks
CoT prompting improves LLM performance on control-flow deobfuscation of C benchmarks, yielding ~16% better CFG reconstruction and ~20.5% better semantic preservation for GPT5 versus zero-shot prompting.
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Large Language Model-Based Agents for Software Engineering: A Survey
A literature survey that collects and categorizes 124 papers on LLM-based agents for software engineering from SE and agent perspectives.