TailTracer: Continuous Tail Tracing for Production Use

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.

Authors

Institutions

Publication Details

Journal
Proceedings of the ACM on Programming Languages
Published
2025-10-09
DOI
https://doi.org/10.1145/3763109
Primary Topic
Software Testing and Debugging Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

TailTracer: Continuous Tail Tracing for Production Use

Tianyi Liu, Rongxin Wu, Zhuangda Wang, Zhiqiang Zuo et al.
Proceedings of the ACM on Programming Languages
Software Testing and Debugging Techniques
article

TailTracer: Continuous Tail Tracing for Production Use

Tianyi Liu, Rongxin Wu, Zhuangda Wang, Zhiqiang Zuo, Xuandong Li, Yi Li, Yiyu Zhang
article en

Abstract

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.

Proceedings of the ACM on Programming LanguagesVol. 9(OOPSLA2)
Xiamen University (CN), Nanjing University (CN)
Openalex Percentile: Top 71%
Software Testing and Debugging Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

TailTracer: Continuous Tail Tracing for Production Use — Tianyi Liu, Rongxin Wu, et al. · Proceedings of the ACM on Programming Languages (2025) | TGRS Research Map | TGRS