AI 中文总结
本文反思软件测量的基础，指出AI智能体使用工具产生的轨迹违背了原始假设，提出开展AI辅助复现计划以开发适应当前数据的有效方法，维持软件测量的相关性。
AI 中文摘要
在过去六十年的大部分时间里，软件测量依赖劳动密集型的专有数据手动收集，这阻碍了该领域的发展。转向重新利用版本控制及相关工具的轨迹，大幅扩大了数据可用性——尤其是随着开源软件的兴起——但这一转变依赖于一个通常未明确说明的假设：这些工具由专业开发者用于构建真实的软件系统。然而，随着轨迹生成工具、数据类型与规模以及实证方法的发展，人们已明确认识到，数据生成与分析方法的变化会影响许多关于软件开发、维护与演化的先前发现。如今AI智能体正积极使用这些相同的工具，由此产生的轨迹常常违背了人类起源的原始假设。为了保持软件测量研究的相关性，需要立即采取行动：我们必须检测当代数据中基础假设被违背的情况，并开发在变化环境下仍有效的新方法。为此，我们提出一个系统性的AI辅助复现计划，利用现代技术重新审视关键发现，旨在产生能在当前数据上获得一致结果的方法，以维持软件测量的意义。
英文摘要
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$\unicode{x2014}$especially with the rise of open-source software$\unicode{x2014}$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.
CommentsAccepted to the 41st IEEE/ACM International Conference on Automated Software Engineering (ASE '26) $\unicode{0x000A}$ DOI: https://doi.org/10.1145/3832783.3834553
Journal refProceedings of the 41st IEEE/ACM International Conference on Automated Software Engineering (ASE '26), October 2026, Munich, Germany. ACM
