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.
Original language | English |
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Title of host publication | ESEC/FSE'20 - Proceedings of the 28th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering |
Editors | Prem Devanbu, Myra Cohen, Thomas Zimmermann |
Place of Publication | New York NY USA |
Publisher | Association for Computing Machinery (ACM) |
Pages | 1566-1570 |
Number of pages | 5 |
ISBN (Electronic) | 9781450370431 |
DOIs | |
Publication status | Published - 2020 |
Event | Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering 2020 - Virtual, United States of America Duration: 8 Nov 2020 → 13 Nov 2020 Conference number: 28th https://dl.acm.org/doi/proceedings/10.1145/3368089 (Proceedings) https://2020.esec-fse.org (Website) |
Conference
Conference | Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering 2020 |
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Abbreviated title | ESEC/FSE 2020 |
Country/Territory | United States of America |
City | Virtual |
Period | 8/11/20 → 13/11/20 |
Internet address |
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Keywords
- Distributed system
- Dynamic slicing
- Reinforcement learning