一句话：提出两阶段边界锚定索引框架，加速在线值流查询并减少冗余搜索与 SMT 调用。
高精度值流分析正转向由审计人员和 AI 智能体触发的在线临时查询，但离线制表不可行，无记忆在线搜索又冗余探索和频繁调用 SMT。SPONGE 提出两阶段边界锚定索引框架：离线用自适应深度策略在关键过程边界选择性预计算可行值流片段，按遍历概率与搜索复杂度分配 SMT；在线用索引引导的惰性下推搜索拼接预验证片段，剪除不可满足路径。在 9 个 C/C++ 项目（最大 380 万行）上，95 分位在线查询时间从近 270 秒降至 50 秒以下；自适应策略比均匀策略减少 75% 离线索引时间，复杂查询工作负载下少于 300 次查询即可摊销成本。
The application of high-precision value-flow analysis is experiencing a paradigm shift from planned executions to online ad hoc queries driven by human auditors and AI agents. However, existing techniques struggle in this interactive setting: exhaustive offline tabulation is fundamentally intractable, while memoryless online search suffers from redundant exploration and SMT invocations. To bridge this gap, we propose SPONGE, a novel two-phase framework that accelerates ad hoc queries through boundary-anchored indexing. Offline, SPONGE employs an adaptive-depth strategy to selectively precompute feasible value-flow segments at critical procedure boundaries, optimizing SMT allocation based on traversal probability and search space complexity. Online, it utilizes an index-guided push-down search with lazy expansion to dynamically stitch these pre-verified segments, effectively bypassing redundant state exploration and pruning unsatisfiable paths. We evaluated SPONGE on 9 C/C++ projects (up to 3.8 million LoC). Results demonstrate that SPONGE drops the 95th-percentile online query time from nearly 270 s to under 50 s compared to a baseline search. Furthermore, the adaptive strategy reduces offline indexing time by 75% over a uniform approach, amortizing the offline cost in fewer than 300 queries for workloads dominated by complex queries.
@article{paperbot12084,
title = {SPONGE: Adaptive Boundary-Anchored Indexing for Online Value-Flow Queries},
author = {Sixiang Peng and Chenyang Sun and Wei Chen and Bowen Zhang and Charles Zhang},
journal = {Proceedings of the ACM on Programming Languages},
volume = {10},
number = {OOPSLA2},
year = {2026},
doi = {10.1145/3839472}
}