Title
WATCHER: in-situ failure diagnosis
Abstract
Diagnosing software failures is important but notoriously challenging. Existing work either requires extensive manual effort, imposing a serious privacy concern (for in-production systems), or cannot report sufficient information for bug fixes. This paper presents a novel diagnosis system, named WATCHER, that can pinpoint root causes of program failures within the failing process ("in-situ"), eliminating the privacy concern. It combines identical record-and-replay, binary analysis, dynamic analysis, and hardware support together to perform the diagnosis without human involvement. It further proposes two optimizations to reduce the diagnosis time and diagnose failures with control flow hijacks. WATCHER can be easily deployed, without requiring custom hardware or operating system, program modification, or recompilation. We evaluate WATCHER with 24 program failures in real-world deployed software, including large-scale applications, such as Memcached, SQLite, and OpenJPEG. Experimental results show that WATCHER can accurately identify the root causes in only a few seconds.
Year
DOI
Venue
2020
10.1145/3428211
Proceedings of the ACM on Programming Languages
Keywords
DocType
Volume
Failure Diagnosis,In-Situ Diagnosis,Root Cause Analysis
Journal
4
Issue
ISSN
Citations 
OOPSLA
2475-1421
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Hongyu Liu100.34
Sam Silvestro2152.61
Xiangyu Zhang32857151.00
Jian Huang400.68
Tongping Liu511.02