As software projects grow increasingly more complex, debloating gains traction. While static analyses yield a coarse over-approximation of reachable code, approaches based on dynamic execution traces risk program correctness. By allowing the developer to reconsider only a few methods and still achieve a significant reduction in code size, cut-based debloating can minimize the risk. In this paper, we propose the idea of finding small cuts in the rule graphs produced by static analysis. After introducing an analysis with suitable semantics, we discuss how to encode its rules into a directed hypergraph. We then present an algorithm for efficiently finding the most effective single cut in the graph. The execution time of the proposed operations allows for the deployment in interactive tools. Finally, we show that our graph model is able to expose methods worthwhile to reconsider.

When IP-packet processing is unconditionally carried out on behalf of an Operating System kernel thread, processing systems can experience overload in high incoming traffic scenarios. This is especially worrying for embedded real-time devices controlling their physical environment in industrial IoT scenarios and automotive systems.We propose an embedded real-time aware IP stack adaption with an early demultiplexing scheme for incoming packets and subsequent per-flow aperiodic scheduling. By instrumenting existing embedded IP stacks, rigid prioritization with minimal latency is deployed without the need of further task resources. Simple mitigation techniques can be applied to individual flows, causing hardly measurable overhead while at the same time protecting the system from overload conditions. Our IP stack adaption is able to reduce the low-priority packet processing time by over 86% compared to an unmodified stack. The network subsystem can thereby remain active at a 7x higher general traffic load before disabling the receive IRQ as a last resort to assure deadlines.