← 研踪 PaperTrailarXiv:2611.02349 · v1
APG: Agentic Static-Analysis Infrastructure on LLVM-IR Graph Databases
首次提交 2026-11-02 08:54
版本更新 2026-11-02 08:54
雷达观测 2026-11-04 23:42
摘要
LLM-based coding agents increasingly rely on external program knowledge, yet access to static-analysis graphs alone does not ensure appropriate operation selection or evidence interpretation. We present APG, an infrastructure for semantics-grounded static program analysis. Built on SVF and LLVM, it decomposes compiler-derived program analyses into atomic capabilities that LLM agents can select, compose, and orchestrate for goal-directed, cross-graph investigation, with query safeguards and evidence traces preserving inspectable results and provenance. APG provides 38 atomic capabilities. On analysis requests arising during benchmark repair runs, 35 achieve first-attempt query-generation success rates above 50%, including 13 at 100%. Evaluation on 812 C/C++ defects shows higher repair success than the evaluated baselines. Compared with Passive Graph Push, APG improves first-iteration repair success by 17.6 and 18.0 percentage points on the two benchmark collections and reduces average LLM input-token consumption by 90.8%. Our tool is available at https://github.com/JasonZhongZexin/APG-ICSE-Demo and its demonstration video at https://youtu.be/vkuExZUjGnw
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v1 · 2026-11-02 08:54 · 最新观测版本
APG: Agentic Static-Analysis Infrastructure on LLVM-IR Graph Databases
LLM-based coding agents increasingly rely on external program knowledge, yet access to static-analysis graphs alone does not ensure appropriate operation selection or evidence interpretation. We present APG, an infrastructure for semantics-grounded static program analysis. Built on SVF and LLVM, it decomposes compiler-derived program analyses into atomic capabilities that LLM agents can select, compose, and orchestrate for goal-directed, cross-graph investigation, with query safeguards and evidence traces preserving inspectable results and provenance. APG provides 38 atomic capabilities. On analysis requests arising during benchmark repair runs, 35 achieve first-attempt query-generation success rates above 50%, including 13 at 100%. Evaluation on 812 C/C++ defects shows higher repair success than the evaluated baselines. Compared with Passive Graph Push, APG improves first-iteration repair success by 17.6 and 18.0 percentage points on the two benchmark collections and reduces average LLM input-token consumption by 90.8%. Our tool is available at https://github.com/JasonZhongZexin/APG-ICSE-Demo and its demonstration video at https://youtu.be/vkuExZUjGnw
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