一句话：提出 mk-Tails 与基于 past-equivalence 的约简，精细权衡模型推断的规模与准确率。
从执行日志推断有限状态模型时，模型规模与准确率存在权衡：大模型更准确但难以呈现和理解，小模型易理解但准确率较低。本文在经典 k-Tails 模型推断算法背景下研究该权衡。作者首先提出 mk-Tails，将 k-Tails 从单一参数推广到多参数，从而能细粒度控制规模与准确率；随后基于 past-equivalence 对 mk-Tails 进行约简，在不降低准确率的情况下有效缩小模型。作者实现了方法，并在真实日志以及文献中的模型和生成日志上评估其性能与有效性。
Many works infer finite-state models from execution logs. Large models are more accurate but also more difficult to present and understand. Small models are easier to present and understand but are less accurate. In this work we investigate the tradeoff between model size and accuracy in the context of the classic k-Tails model inference algorithm. First, we define mk-Tails, a generalization of k-Tails from one to many parameters, which enables fine-grained control over the tradeoff. Second, we extend mk-Tails with a reduction based on past-equivalence, which effectively reduces the size of the model without decreasing its accuracy. We implemented our work and evaluated its performance and effectiveness on real-world logs as well as on models and generated logs from the literature.
DOI 原文 ·
@inproceedings{paperbot5263,
title = {Size and Accuracy in Model Inference},
author = {Nimrod Busany and Shahar Maoz and Yehonatan Yulazari},
booktitle = {ASE},
year = {2019},
doi = {10.1109/ase.2019.00087}
}