@inproceedings{c7ab285590134486af00d4a621f0f200,
title = "Dads: Dynamic slicing continuously-running distributed programs with budget constraints",
abstract = "We present Dads, the first distributed, online, scalable, and cost-effective dynamic slicer for continuously-running distributed programs with respect to user-specified budget constraints. Dads is distributed by design to exploit distributed and parallel computing resources. With an online analysis, it avoids tracing hence the associated time and space costs. Most importantly, Dads achieves and maintains practical scalability and cost-effectiveness tradeoffs according to a given budget on analysis time by continually and automatically adjusting the configuration of its analysis algorithm on the fly via reinforcement learning. Against eight real-world Java distributed systems, we empirically demonstrated the scalability and cost-effectiveness merits of Dads. The open-source tool package of Dads with a demo video is publicly available.",
keywords = "Distributed system, Dynamic slicing, Reinforcement learning",
author = "Xiaoqin Fu and Haipeng Cai and Li Li",
note = "Publisher Copyright: {\textcopyright} 2020 Owner/Author.; 28th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2020 ; Conference date: 08-11-2020 Through 13-11-2020",
year = "2020",
month = nov,
day = "8",
doi = "10.1145/3368089.3417920",
language = "English",
series = "ESEC/FSE 2020 - Proceedings of the 28th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering",
publisher = "Association for Computing Machinery, Inc",
pages = "1566--1570",
editor = "Prem Devanbu and Myra Cohen and Thomas Zimmermann",
booktitle = "ESEC/FSE 2020 - Proceedings of the 28th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering",
}