一句话：提出以推理为中心的性能调试观，用“推理表面”维护多个经验性解释，将准确率降为约束而非唯一目标。
论文指出，微服务性能调试常被建模为以准确率为目标的优化问题，追求单一“最佳”根因，这形成了“优化陷阱”：忽视生产环境中证据不完整、故障相互交织、工程师需在不确定下推理的现实。作者主张转向以推理为中心的视角：调试系统应生成、经验性挑战并整理多个假设，把解释视为一等工件，准确率仅作为约束。为此提出“推理表面”这一概念性调试工件，维护多个有经验依据的解释，显式呈现不确定性与权衡，而非将诊断压缩为单一不透明结果。通过初步实例化，展示该视角在运维压力下带来性质不同的调试行为，并呼吁从单一数值排行榜转向支持以人为中心推理的系统。
Performance debugging in microservice systems is commonly framed as an optimization problem that seeks a single "best" root cause under accuracy-driven objectives. We argue that this framing creates an optimization trap: it obscures the reality of production environments, where evidence is incomplete, failures interact, and engineers must reason under uncertainty. This paper advocates a reasoning-centered perspective in which debugging systems generate, empirically challenge, and curate multiple hypotheses, treating explanations as first-class artifacts and accuracy as a constraint rather than the sole goal. We introduce the notion of a Reasoning Surface—a conceptual debugging artifact that maintains multiple, empirically grounded explanations and makes uncertainty and trade-offs explicit, rather than collapsing diagnosis into a single opaque result. Through an initial instantiation, we illustrate how this perspective leads to qualitatively different debugging behavior, particularly under operational stress, and argue for a shift beyond single-number leaderboards toward systems that support human-centered reasoning.
@inproceedings{PanahandehEH26,
title = {Rethinking Performance Debugging: From Optimization to Collaborative Reasoning},
author = {Mahsa Panahandeh and Naser Ezzati-Jivan and Abdelwahab Hamou-Lhadj},
booktitle = {FSE},
year = {2026},
doi = {10.1145/3803437.3805578}
}