一句话：通过工业合作开发 ML 增强追踪分析工具，提出以采用为中心的设计原则并验证认知兼容、嵌入专业知识与透明信任。
系统追踪对理解复杂软件行为至关重要，但复杂追踪分析工具在工业界采用不足。作者与 Ericsson Montreal 合作一年开发 ML 增强的 Eclipse TMLL，发现障碍在于专家知识难转化、分析难融入工作流、自动结果难信任，并提出“卓越悖论”。TMLL 采用以采用为中心的设计：嵌入专家知识、提供透明解释、支持增量采用。通过专家反馈、Eclipse 集成及 40 名业界与学界人士调研评估，77.5% 更重视结果质量与信任而非技术复杂度，67.5% 偏好带用户控制的半自动分析，验证认知兼容、嵌入专业知识和透明信任三原则。
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.
DOI 原文 ·
@inproceedings{paperbot9295,
title = {From Technical Excellence to Practical Adoption: Lessons Learned Building an ML-Enhanced Trace Analysis Tool},
author = {Kaveh Shahedi and Matthew Khouzam and Heng Li and Maxime Lamothe and Foutse Khomh},
booktitle = {ASE},
year = {2025},
doi = {10.1109/ase63991.2025.00286}
}