Name
Abstract | ||
|---|---|---|
The behavior of complex software-intensive systems of systems often only fully emerges during operation, when all systems interact with each other and with their environment. Runtime monitoring approaches are thus used to detect deviations from the expected behavior, which is commonly defined by engineers, e.g., using temporal logic or domain-specific languages. However, the deep domain knowledge required to specify constraints is often not available during the development of systems of systems with multiple teams independently working on heterogeneous components. In this paper, we thus describe our ongoing PhD research to automatically mine constraints for runtime monitoring from recorded events. Our approach mines constraints on event occurrence, timing, data, and combinations of these properties. The approach further presents the mined constraints to users offering multiple ranking strategies and can also be used to support users in system evolution scenarios.
|
Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3238147.3241532 | ASE |
Keywords | Field | DocType |
Monitoring, constraint mining, systems of systems | Monitoring system,Software engineering,Domain knowledge,Systems engineering,Ranking,Computer science,System of systems,Temporal logic | Conference |
ISSN | ISBN | Citations |
1527-1366 | 978-1-4503-5937-5 | 1 |
PageRank | References | Authors |
0.36 | 14 | 1 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thomas Krismayer | 1 | 10 | 2.51 |