Anomaly Detection for Debugging is a methodological framework within distributed debugging that identifies deviations from expected system behavior by analyzing execution traces, logs, or performance metrics to isolate faults in concurrent and distributed systems.
24
Publications
1.6K
Citations
86
Authors
36
Institutions
Publications per year
2017–2026
5
Parents
Publications
24
Automatic identification of load testing problems
Zhen Ming Jiang, Ahmed E. Hassan, Gilbert Hamann et al. · 2008 · 94 citations
The Good, the Bad, and the Differences
Ang Chen, Yang Wu, Andreas Haeberlen et al. · 2016 · 51 citations · Full text
1–5 of 24
Authors
86
- SB
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- IL
2
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- SH
2
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- MS
2
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- KK
1
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1–5 of 86
Institutions
36
Lawrence Livermore National Laboratory
Livermore, United States
2 h-index
7 publications
212 citations
Stanford University
Stanford, United States
2 h-index
2 publications
864 citations
Purdue University West Lafayette
West Lafayette, United States
2 h-index
8 publications
150 citations
University of Southern California
Los Angeles, United States
1 h-index
4 publications
152 citations
Hasso Plattner Institute
Potsdam, Germany
1 h-index
1 publications
17 citations
Microsoft (United States)
Redmond, United States
1 h-index
2 publications
64 citations
Tsinghua University
Beijing, China
1 h-index
1 publications
11 citations
Institute for System Programming
Moscow, Russia
1 h-index
4 publications
68 citations
1–8 of 36
Venues
- publish.UP (University of Potsdam)
Repository · 1 publications · 17 citations
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- IEEE Transactions on Software Engineering
TSE · 1 publications · 85 citations
1
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- International Journal of Scientific Research in Computer Science Engineering and Information Technology
1 publications · 27 citations
1
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- Information Sciences
1 publications · 25 citations
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