发现性能原型：关键路径感知的模式分析与回归检测
Discovering Performance Archetypes: Critical-Path-Aware Pattern Analysis and Regression Detection
- Polytechnique Montréal（蒙特利尔理工学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出关键路径感知的性能分析方法，发现13种跨应用性能原型，并基于此构建多信号回归检测框架，F1分数达0.867，较资源仅方法提升60.4%。
AI中文摘要：
软件性能分析与预测需要整合多种信号，因为仅凭代码结构无法捕捉由执行频率、资源争用和I/O模式所塑造的运行时行为。我们提出了一种关键路径感知的性能分析方法，通过综合静态代码特征、动态执行轨迹和内核级资源数据，自动发现重复出现的性能模式。在一项涵盖六个真实世界C/C++应用程序（SQLite、OpenSSL、Zstandard、FFmpeg、cURL和jq）的初步研究中，我们首先通过实验证实，静态复杂度指标仅能解释关键路径执行时间中10.4%的方差（$\ ho^2$），从而量化了这一虽在理论上可预期但此前尚未跨应用程序系统测量的差距。受此发现启发，我们分析了近80,000条关键执行路径，并解决两个研究问题。首先，我们发现了13种不同的性能原型：在不同应用程序中一致出现的重复行为模式，且与它们的领域或实现无关。其中五种模式近乎普遍，出现在所研究的六个应用程序中的至少五个中。值得注意的是，其中三种原型在所有六个应用程序中均存在，且这些常见模式合计占所有观测路径的56.4%。每种原型对应特定的资源画像和可跨领域迁移的优化策略。其次，我们在一个多信号回归检测框架中利用这些原型，该框架对路径结构、资源消耗和原型偏差进行三角验证，取得了0.867的F1分数，相比仅依赖资源的方法提升了60.4%。
英文摘要：
Software performance analysis and prediction requires integrating multiple signals, as code structure alone cannot capture runtime behavior shaped by execution frequency, resource contention, and I/O patterns. We present a critical-path-aware performance analysis methodology that automatically discovers recurring performance patterns by synthesizing static code features, dynamic execution traces, and kernel-level resource data. In a preliminary study across six real-world C/C++ applications (SQLite, OpenSSL, Zstandard, FFmpeg, cURL, and jq), we first empirically confirm that static complexity metrics explain only 10.4% of the variance ($ρ^2$) in critical path execution time, quantifying a gap that, while theoretically expected, had not been measured systematically across applications. Motivated by this finding, we analyze nearly 80,000 critical execution paths and address two research questions. First, we discover 13 distinct performance archetypes: recurring behavioral patterns that appear consistently across different applications, independent of their domain or implementation. Five of these patterns are near-universal and appear in at least five of the six applications studied. Notably, three of these archetypes are present in all six applications, and together, these common patterns account for 56.4% of all observed paths. Each archetype maps to specific resource profiles and optimization strategies that transfer across domains. Second, we leverage these archetypes within a multi-signal regression detection framework that triangulates path structure, resource consumption, and archetype deviations, achieving an F1-score of 0.867 and a 60.4% improvement over resource-only methods.
