In recent years, the rapid growth of network traffic and the performance bottlenecks inherent in kernel networking stacks have driven the widespread adoption of userspace networking frameworks. While kernel-bypass solutions such as the Data Plane Development Kit (DPDK) effectively eliminate kernel overhead, they also limit observability for traditional monitoring tools, complicating fault diagnosis and performance tuning. This observability gap, coupled with the complexity of modern packet-processing software, makes diagnosing performance issues increasingly difficult. This paper presents a performance analysis framework tailored for DPDK-based applications. The framework leverages trace data collected through DPDK's native tracer to derive targeted performance metrics, which are visualized through interactive, domain-specific analyses in Trace Compass. By enabling fine-grained observability with minimal runtime overhead, the approach bridges the gap between low-level tracing and actionable performance insights. To ground our design in real-world needs, we surveyed 19 industry practitioners to validate our design choices and capture empirical evidence of the debugging challenges encountered when diagnosing DPDK-based applications. We further demonstrate how the proposed analyses can reveal and explain performance bottlenecks in a widely used software router.

Dynamic Binary Instrumentation (DBI) in user space often suffers from low probe insertion success rates and high execution overhead, due to challenges in handling the compact instruction layouts ($\lt $5 bytes) and complex trampoline placement constraints. Existing techniques are either limited in scope, incur high runtime overhead, or rely on heavyweight code relocation. This paper introduces NOProbe, a lightweight, user-space DBI framework that enables safe and efficient probe insertion using two novel strategies. The first strategy locates trampoline sites by leveraging compiler-generated NOP paddings; the second employs pseudo-NOP instructions to support trampoline placement even when instructions overlap. Additionally, we propose a thread-safe patching algorithm,lock-redirect-load-arm, for safe runtime code modification. Experimental results show that NOProbe achieves 97%-99% probe effectiveness, reduces probe insertion latency, and maintains very low per-probe execution overhead, even under high probe density and multithreaded workloads.

The complexity of the Ethereum smart contracts makes it challenging to avoid security flaws. This problem led to many code analysis tools, which detect potential flaws and report them textually. However, the lack of context and visual information in these reports hinders the stakeholders' under-standing of the detailed information. Visualization can assist a developer in grasping such context, but current state-of-the-art visualization tools provide only fixed and limited visualization types. To this end, we present Contract Viz, based on the versatile platform Eclipse Trace Compass (TC), which supports various views and analyses in parallel. Our contribution enables TC to visualize Ethereum transaction traces using flame charts and gas consumption plots. This reveals information on account activities and provides insights into the correct or possibly flawed behaviors. GitHub repo-https://github.com/AisXiaolinlContractViz You Tube video-https://aisxiaolin.github.io/VideoDemo/