论文标题
从长执行轨迹中学习简洁的模型
Learning Concise Models from Long Execution Traces
论文作者
论文摘要
系统级行为的摘要模型在设计探索,分析,测试和验证中都有应用。我们将一种用于自动提取有用模型的新算法描述为自动机,该算法是由由软件驱动的HW/SW系统的执行轨迹,该软件驱动了感兴趣的用例。我们的算法利用现代程序合成技术在自动机边缘生成谓词,简洁地描述了系统行为。它采用痕量细分来解决长距离痕迹的复杂性。我们学习捕获交易级,全系统范围行为的简洁模型 - 经常证明使用来自各种来源的痕迹,包括X86 QEMU虚拟平台和实时Linux内核。
Abstract models of system-level behaviour have applications in design exploration, analysis, testing and verification. We describe a new algorithm for automatically extracting useful models, as automata, from execution traces of a HW/SW system driven by software exercising a use-case of interest. Our algorithm leverages modern program synthesis techniques to generate predicates on automaton edges, succinctly describing system behaviour. It employs trace segmentation to tackle complexity for long traces. We learn concise models capturing transaction-level, system-wide behaviour--experimentally demonstrating the approach using traces from a variety of sources, including the x86 QEMU virtual platform and the Real-Time Linux kernel.