Affiliation:
1. University of Illinois at Urbana-Champaign and University of California, San Diego
2. University of California, San Diego
Abstract
Diagnosing software failures in the field is notoriously difficult, in part due to the fundamental complexity of troubleshooting
any
complex software system, but further exacerbated by the paucity of information that is typically available in the production setting. Indeed, for reasons of both overhead and privacy, it is common that only the run-time log generated by a system (e.g., syslog) can be shared with the developers. Unfortunately, the ad-hoc nature of such reports are frequently insufficient for detailed failure diagnosis. This paper seeks to improve this situation within the rubric of existing practice. We describe a tool,
LogEnhancer
that automatically “enhances” existing logging code to aid in future post-failure debugging. We evaluate
LogEnhancer
on eight large, real-world applications and demonstrate that it can dramatically reduce the set of potential root failure causes that must be considered while imposing negligible overheads.
Funder
Division of Computer and Network Systems
Division of Computing and Communication Foundations
Publisher
Association for Computing Machinery (ACM)
Reference61 articles.
1. Performance debugging for distributed systems of black boxes
2. An overview of the saturn project
3. Apple. 2004. Apple Inc. CrashReport. Tech. rep. TN2123. Apple. 2004. Apple Inc. CrashReport. Tech. rep. TN2123.
4. TraceBack
Cited by
79 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Log statements generation via deep learning: Widening the support provided to developers;Journal of Systems and Software;2024-04
2. LogShrink: Effective Log Compression by Leveraging Commonality and Variability of Log Data;Proceedings of the 46th IEEE/ACM International Conference on Software Engineering;2024-02-06
3. Adonis
: Practical and Efficient Control Flow Recovery through OS-level Traces;ACM Transactions on Software Engineering and Methodology;2023-11-24
4. AutoLog: A Log Sequence Synthesis Framework for Anomaly Detection;2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE);2023-09-11
5. Multi-level Adaptive Execution Tracing for Efficient Performance Analysis;2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA);2023-05-23