一句话：提出持续尾部追踪，仅保留执行轨迹末段，低开销地在生产环境完整记录函数调用与返回序列。
论文关注生产环境中 bug 逃逸后难以复现与诊断的问题：在测试之外部署的软件发生故障时，需要尽可能完整的执行信息，但追踪能力、运行时开销与轨迹规模之间存在尖锐矛盾，海量轨迹数据会给 I/O、存储与网络传输带来巨大压力。为此作者提出面向生产的“持续尾部追踪”，不再只捕获崩溃调用栈，而是生成完整的函数调用与返回序列，同时仅保留轨迹的最后一段以控制数据量。为支撑这一设计，论文提出一种新型 trace decoder 实现精确的尾部轨迹解码，并设计基于路径的插桩点选择算法以降低开销。作者在 LLVM 之上实现了工具 TailTracer，并在 SPEC CPU 2017 基准套件、开源数据库系统以及真实 bug 上开展评估，结果表明 TailTracer 在实现低开销尾部追踪的同时，提供了比基线更具信息量的轨迹数据。
Despite extensive in-house testing, bugs often escape to deployed software. Whenever a failure occurs in production software, it is desirable to collect as much execution information as possible so as to help developers reproduce, diagnose and fix the bug. To reconcile the tension between trace capability, runtime overhead, and trace scale, we propose continuous tail tracing for production use. Instead of capturing only crash stacks, we produce the complete sequence of function calls and returns. Importantly, to avoid the overwhelming stress to I/O, storage, and network transfer caused by the tremendous amount of trace data, we only retain the final segment of trace. To accomplish it, we design a novel trace decoder to support precise tail trace decoding, and an effective path-based instrumentation-site selection algorithm to reduce overhead. We implemented our approach as a tool called TailTracer on top of LLVM, and conducted the evaluations over the SPEC CPU 2017 benchmark suite, the open-source database system, and real-world bugs. The experimental results validate that TailTracer achieves low-overhead tail tracing, while providing more informative trace data than the baseline.
@article{paperbot2917,
title = {TailTracer: Continuous Tail Tracing for Production Use},
author = {Tianyi Liu and Yi Li and Yiyu Zhang and Zhuangda Wang and Rongxin Wu and Xuandong Li and Zhiqiang Zuo},
journal = {Proceedings of the ACM on Programming Languages},
volume = {9},
number = {OOPSLA2},
year = {2025},
doi = {10.1145/3763109}
}