Name
Abstract | ||
|---|---|---|
Kernel traces are sequences of low-level events comprising a name and multiple arguments, including a timestamp, a process id, and a return value, depending on the event. Their analysis helps uncover intrusions, identify bugs, and find latency causes. However, their effectiveness is hindered by omitting the event arguments. To remedy this limitation, we introduce a general approach to learning a r... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/MSR52588.2021.00025 | 2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR) |
Keywords | DocType | ISSN |
Deep learning,Neural networks,Computer bugs,Companies,Software,Encoding,Servers | Conference | 2160-1852 |
ISBN | Citations | PageRank |
978-1-7281-8710-5 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Quentin Fournier | 1 | 0 | 0.34 |
| Daniel Aloise | 2 | 344 | 24.21 |
| Seyed Vahid Azhari | 3 | 0 | 0.34 |
| François Tetreault | 4 | 0 | 0.34 |