Anticipatory Recurrent Monitoring with Uncertainty and Assumptions
| Title | Anticipatory Recurrent Monitoring with Uncertainty and Assumptions |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Kallwies, H, Leucker, M, Sánchez, C, Scheffel, T |
| Conference Name | 22nd International Conference on Runtime Verification (RV) |
| Date Published | 09/2022 |
| Publisher | Springer International Publishing |
| Conference Location | Tbilisi, Georgia |
| Keywords | Anticipation, Assumptions, Recurrent Runtime Verification, Uncertainty |
| Abstract | Runtime Verification is a lightweight verification approach that aims at checking that a run of a system under observation adheres to a formal specification. A classical approach is to synthesize a monitor from an LTL property. Usually, such a monitor receives the trace of the system under observation incrementally and checks the property with respect to the first position of any trace that extends the received prefix. This comes with the disadvantage that once the monitor detects a violation or satisfaction of the verdict it cannot recover and the erroneous position in the trace is not explicitly disclosed. An alternative monitoring problem, proposed for example for Past LTL evaluation, is to evaluate the LTL property repeatedly at each position in the received trace, which enables recovering and gives more information when the property is breached. In this paper we study this concept of recurrent monitoring in detail, particularly we investigate how the notion of anticipation (yielding future verdicts when they are inevitable) can be extended to recurrent monitoring. Furthermore, we show how two fundamental approaches in Runtime Verification can be applied to recurrent monitoring, namely Uncertainty—which deals with the handling of inaccurate or unavailable information in the input trace—and Assumptions, i.e. the inclusion of additional knowledge about system invariants in the monitoring process. |
| URL | https://link.springer.com/chapter/10.1007/978-3-031-17196-3_10 |
| DOI | 10.1007/978-3-031-17196-3_10 |
@inproceedings {1418,
title = {Anticipatory Recurrent Monitoring with Uncertainty and Assumptions},
booktitle = {22nd International Conference on Runtime Verification (RV)},
year = {2022},
month = {09/2022},
publisher = {Springer International Publishing},
organization = {Springer International Publishing},
address = {Tbilisi, Georgia},
abstract = {<p>Runtime Verification is a lightweight verification approach that aims at checking that a run of a system under observation adheres to a formal specification. A classical approach is to synthesize a monitor from an LTL property. Usually, such a monitor receives the trace of the system under observation incrementally and checks the property with respect to the first position of any trace that extends the received prefix. This comes with the disadvantage that once the monitor detects a violation or satisfaction of the verdict it cannot recover and the erroneous position in the trace is not explicitly disclosed. An alternative monitoring problem, proposed for example for Past LTL evaluation, is to evaluate the LTL property repeatedly at each position in the received trace, which enables recovering and gives more information when the property is breached. In this paper we study this concept of recurrent monitoring in detail, particularly we investigate how the notion of anticipation (yielding future verdicts when they are inevitable) can be extended to recurrent monitoring. Furthermore, we show how two fundamental approaches in Runtime Verification can be applied to recurrent monitoring, namely Uncertainty{\textemdash}which deals with the handling of inaccurate or unavailable information in the input trace{\textemdash}and Assumptions, i.e. the inclusion of additional knowledge about system invariants in the monitoring process.</p>
},
keywords = {Anticipation, Assumptions, Recurrent Runtime Verification, Uncertainty},
doi = {10.1007/978-3-031-17196-3_10},
url = {https://link.springer.com/chapter/10.1007/978-3-031-17196-3_10},
author = {Hannes Kallwies and Martin Leucker and C{\'e}sar S{\'a}nchez and Torben Scheffel}
}