Current industrial trends require flexible and reconfigurable manufacturing systems, with Cyber-Physical Production Systems (CPPS) playing a crucial role. However, the increased flexibility and decentralisation increase the risk of unexpected or emerging system behaviour. Message sequence charts (MSCs) have proven helpful in the early stages of system design to capture intended scenarios. This paper introduces a process that utilises MSCs to identify unexpected system behaviour. After synthesising the MSCs into finite state machines, the process suggests applying Kronecker algebra to verify intended and identify unintended scenarios. The process is illustrated by an example depicting an auto-lock mechanism. The paper concludes by outlining further research focusing on implementing the process and identifying emergence at runtime.

String pattern matching with finite automata (FAs) is a well-established method across many areas in computer science. Until now, data dependencies inherent in the pattern matching algorithm have hampered effective parallelization. To overcome the dependency-constraint between subsequent matching steps, simultaneous deterministic finite automata (SFAs) have been recently introduced. Although an SFA facilitates parallel FA matching, SFA construction itself is limited by the exponential state-growth problem, which makes sequential SFA construction intractable for all but the smallest problem sizes.In this paper, we propose several optimizations to leverage parallelism, improve cache and memory utilization and greatly reduce the processing steps required to construct an SFA. We introduce fingerprints and hashing for efficient comparisons of SFA states. Kernels of ×86 SIMD-instructions facilitate cache-locality and leverage data-parallelism with the construction of SFA states. Our parallelization for shared-memory multicores employs lock-free synchronization to minimize cache-coherence overhead. Our dynamic work-partitioning scheme employs work-stealing with thread-local work-queues. The structural properties of FAs allow efficient compression of SFA states. Our construction algorithm dynamically switches to in-memory compression of SFA states for problem sizes which approach the main memory size limit of a given system.We evaluate our approach with patterns from the PROSITE protein database. We achieve speedups of up to 312× on a 64-core AMD system and 193× on a 44-core (88 hyperthreads) Intel system. Our SFA construction algorithm shows scalability on both evaluation platforms.

It is well accepted that pointers are a common source of memory anomalies such as loosing references to dynamic records without deallocating them (also known as memory leaks). This paper presents a novel pointer analysis framework that detects memory leaks by statically analyzing the behavior of programs.