This project is concerned with continuous system testing and monitoring of system of systems - SoS during the operational execution.
For systems composed of multiple layers of systems, the consequences of a software update are always hard to foresee. An upgrade in a lower layer may trigger faults in applications built on top of it, and as the systems are complex, test environments do not capture all aspects of the complexity. Further, with continuous deployment of new releases, the time devoted to spend on system testing tends to be reduced. Erroneous software – be it by coding, design or specification error – is thus at higher risk of slipping into operations. To mitigate this risk, we propose exploring and approach, where system of systems being continuously monitored, as a kind of continuous system testing.
As a case to explore the continuous system testing in, we consider a ticketing system for public transportation (for example, Skånetrafiken). The project proposal aims to improve system operations and development by continuous monitoring and learning from run-time alerts. The main objectives are to identify sequences/clusters/absence of alerts that may provide information about system health/un-healthiness and to develop technology for predicting failures and analyzing alerts inspired by machine learning.