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.

System tracing has become essential for understanding complex software behavior in modern systems, yet sophisticated trace analysis tools face significant adoption gaps in industrial settings. Through a year-long collaboration with Ericsson Montreal, developing TMLL (Trace-Server Machine Learning Library, now in the Eclipse Foundation), we investigated barriers to trace analysis adoption. Contrary to assumptions about complexity or automation needs, practitioners struggled with translating expert knowledge into actionable insights, integrating analysis into their workflows, and trusting automated results that they could not validate. We identified what we called the Excellence Paradox: technical excellence can actively impede adoption when conflicting with usability, transparency, and practitioner trust. TMLL addresses this through an adoption-focused design that embeds expert knowledge in interfaces, provides transparent explanations, and enables incremental adoption. Validation through Ericsson’s experts’ feedback, Eclipse Foundation’s integration, and a survey of 40 industry and academic professionals revealed consistent patterns: survey results showed that 77.5% prioritize quality and trust in results over technical sophistication, while 67.5% prefer semi-automated analysis with user control, findings supported by qualitative feedback from industrial collaboration and external peer review. Results validate three core principles: cognitive compatibility, embedded expertise, and transparency-based trust. This challenges conventional capability-focused tool development, demonstrating that sustainable adoption requires reorientation toward adoption-focused design with actionable implications for automated software engineering tools.