Using a 61-feature readability model, LLM code matches or slightly exceeds human code in readability score, shows distinct issue patterns, and prompt design has limited influence.
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ICL4Decomp applies in-context learning to guide LLMs in generating re-executable decompiled code from binaries, reporting roughly 40% higher re-executability than prior methods across datasets and optimization levels.
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Characterizing Readability Issue Patterns and the Role of Prompt Design in LLM-Generated Code
Using a 61-feature readability model, LLM code matches or slightly exceeds human code in readability score, shows distinct issue patterns, and prompt design has limited influence.
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Context-Guided Decompilation: A Step Towards Re-executability
ICL4Decomp applies in-context learning to guide LLMs in generating re-executable decompiled code from binaries, reporting roughly 40% higher re-executability than prior methods across datasets and optimization levels.