Name
Abstract | ||
|---|---|---|
Software systems often record important runtime information in system logs for troubleshooting purposes. There have been many studies that use log data to construct machine learning models for detecting system anomalies. Through our empirical study, we find that existing log-based anomaly detection approaches are significantly affected by log parsing errors that are introduced by 1) OOV (out-of-vo... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ASE51524.2021.9678773 | 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE) |
Keywords | DocType | ISBN |
Runtime,Codes,Semantics,Machine learning,Transformers,Software systems,Data models | Conference | 978-1-6654-0337-5 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Van-Hoang Le | 1 | 1 | 0.69 |
| Hongyu Zhang | 2 | 864 | 50.03 |