一句话：提出路径敏感抽象解释缓存分析，维护关键路径缓存状态，在保持可扩展性下平均提升 WCET 精度 24.83%。
WCET 分析为实时系统提供程序执行时间上界，而缓存行为的精确建模对 WCET 估计至关重要，因为缓存缺失延迟通常远高于命中延迟。缓存行为具有路径相关性，现有方法常用抽象解释避免枚举所有路径，但传统抽象解释是上下文无关的，会跨路径采用最保守情形，导致 WCET 上界过度估计。为兼顾可扩展性与精度，本文提出一种路径敏感的抽象解释缓存分析：它维护一组来自关键执行路径的缓存状态，从而导出上下文感知的缓存行为，并可无缝集成到标准 WCET 框架中，得到紧致且可证明可靠的 WCET 界。实验表明，该方法在不牺牲可扩展性的前提下，平均将 WCET 精度提升 24.83%。
Worst-Case Execution Time (WCET) analysis provides an upper bound on a program’s execution time and is fundamental to the design and verification of real-time systems. Accurate modeling of cache behavior is critical for WCET estimation, as cache-miss latency is typically orders of magnitude larger than cache-hit latency. Since cache behavior is path dependent, existing methods commonly use abstract interpretation to estimate cache behaviors without enumerating all paths. However, conventional abstract interpretation is context-agnostic—adopting the most conservative case across paths—and thus may produce an overestimated WCET bound. To bridge the gap between scalability and accuracy, we propose a path-sensitive abstract-interpretation-based cache analysis that maintains a set of cache states drawn from critical execution paths to derive context-aware cache behavior. This path-sensitive cache analysis integrates seamlessly into standard WCET frameworks, resulting in tight yet provably sound WCET bounds. Experiments show that our approach improves WCET accuracy by an average of 24.83% without sacrificing scalability.
@article{XiaoSZZJ26,
title = {Path-Sensitive Abstract Interpretation for WCET Estimation},
author = {Shangshang Xiao and Mengxia Sun and Wei Zhang and Naijun Zhan and Lei Ju},
journal = {Proceedings of the ACM on Programming Languages},
volume = {10},
number = {PLDI},
year = {2026},
doi = {10.1145/3808252}
}