一句话：指出AI代理使用开发工具生成的痕迹违背人类来源假设，呼吁检测假设失效并开展AI辅助复现研究。
过去六十年，软件度量多依赖人工收集专有数据，后转向复用版本控制等工具痕迹，开源兴起极大扩展了数据可得性，但其隐含假设是这些工具由专业开发者用于构建真实系统。随着工具、数据类型与规模及实证方法演变，数据生成与分析方式的变化已影响许多关于软件开发、维护和演化的既有发现；AI代理如今使用相同工具，使痕迹常违反人类来源假设。为保持研究相关性，作者呼吁检测当代数据中基础假设被违反的情形，并开发在新环境下仍有效的方法，提出系统性的AI辅助复现计划，用现代技术重访关键发现，以在当前数据上获得一致结果。
For most of the past six decades, software measurement relied on labor-intensive manual collection of proprietary data, which hampered progress. The shift to repurposing traces from version control and related tools dramatically expanded data availability—especially with the rise of open-source software—but hinged on an often unstated assumption: that these tools are used by professional developers to build genuine software systems. However, as trace-generating tools, data types and scale, and empirical methods have all evolved, it has become clear that changes in data generation and analytical approaches affect many prior findings about software development, maintenance, and evolution. With AI agents now actively using these same tools, the resulting traces frequently violate the original assumption of human origin. To preserve the relevance of software measurement research, immediate action is needed: We must detect when foundational assumptions are violated in contemporary data and develop new methodologies that remain valid under changed circumstances. To this end, we propose a systematic AI-assisted replication program that revisits key findings using modern techniques, aiming for methods that yield consistent results on current data to keep software measurement meaningful.