We present online tunable diagnostic and membership protocols for generic time-triggered (TT) systems to detect crashes, send/receive omission faults, and network partitions. Compared to existing diagnostic and membership protocols for TT systems, our protocols do not rely on the single-fault assumption and also tolerate non-fail-silent (Byzantine) faults. They run at the application level and can be added on top of any TT system (possibly as a middleware component) without requiring modifications at the system level. The information on detected faults is accumulated using a penalty/reward algorithm to handle transient faults. After a fault is detected, the likelihood of node isolation can be adapted to different system configurations, including configurations where functions with different criticality levels are integrated. All protocols are formally verified using model checking. Using actual automotive and aerospace parameters, we also experimentally demonstrate the transient fault handling capabilities of the protocols.

Large software systems typically exist in many revisions. In order to analyze such systems, their evolution needs to be analyzed, too. The main challenge in this context is to cope with the large volume of data and to visualize the system and its evolution as a whole. This paper presents an approach for visualizing the evolution of large-scale systems which uses three different, tightly integrated visualizations that are 3-dimensional and/or animated and which support different analysis tasks. According to a first empirical study, all tasks are supported well by at least one visualization.

We present a tunable diagnostic protocol for generic time-triggered (TT) systems to detect crash and send/receive omission faults. Compared to existing diagnostic and membership protocols for TT systems, it does not rely on the single-fault assumption and tolerates malicious faults. It runs at the application level and can be added on top of any TT system (possibly as a middleware component) without requiring modifications at the system level. The information on detected faults is accumulated using a penalty/reward algorithm to handle transient faults. After a fault is detected, the likelihood of node isolation can be adapted to different system configurations, including those where functions with different criticality levels are integrated. Using actual automotive and aerospace parameters, we experimentally demonstrate the transient fault handling capabilities of the protocol.