LLM Agent-Assisted Reverse Engineering with Quantitative Readability Metrics

Abstract:Automatic decompilers produce functionally correct but often unreadable C code. This paper addresses one stage of the reverse engineering workflow: improving the readability of decompiled code using LLM agents guided by quantitative metrics. We present a three-phase research evolution. Phase 1 (tool-driven steering via Ghidra MCP) suffered from incomplete coverage and inconsistent improvements due to lack of quantitative guidance. Phase 2 (structural similarity validation alone) revealed that agents optimize for metrics in unintended ways, producing structurally equivalent but less readable code. Our contribution is the Quantitative Readability Score (QRS) framework, a composite metric combining a structural similarity gate with three independent readability sub-metrics (Lexical Surprisal, Structural Simplicity, and Idiomatic Quality). We demonstrate that QRS-guided refinement enables LLM agents to make targeted readability improvements without sacrificing correctness. We provide a discussion of the broader reverse engineering workflow (binary lifting, decompilation cleanup, and achieving functional equivalence) as context, however, it remains out of scope.

Software Engineering (cs.SE); Artificial Intelligence (cs.AI)

D.2.8; D.2.7; I.2.11

arXiv:2606.06838 [cs.SE]

(or arXiv:2606.06838v1 [cs.SE] for this version)

https://doi.org/10.48550/arXiv.2606.06838 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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