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Dissertation
Scalable and Cost-Effective Data Flow Analysis for Distributed Software: Algorithms and Applications
Washington State University
Doctor of Philosophy (PhD), Washington State University
01/2022
DOI:
https://doi.org/10.7273/000004554
Handle:
https://hdl.handle.net/2376/125285
Abstract
More and more distributed software systems are being developed and deployed today. Like other software, distributed software systems also need very strong quality assurance support. Distributed software is often very large/complex, has distributed components, and does not have a global clock. All these characteristics make it very challenging to analyze the information flow of such systems to support the software quality assurance. One challenge is that existing dynamic analysis techniques hardly scale to large distributed software systems in the real world. It is also challenging to develop cost-effective dynamic analysis approaches. There are also applicability and portability challenges for dynamic analysis algorithms/applications of distributed software.My dissertation addresses these challenges via three novel approaches to data flow analysis for distributed software. My first approach is based on measuring inter-process communications to understand distributed software behaviors and predict distributed software quality. Then, I developed a particular approach that can actually pinpoint sensitive information via multi-staged and refinement-based dynamic information flow analysis for distributed software. Finally, I explored dynamic dependence analysis for distributed systems, utilizing reinforcement learning to automatically adjust analysis configurations for scalability and better cost-effectiveness tradeoffs.
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Details
- Title
- Scalable and Cost-Effective Data Flow Analysis for Distributed Software
- Creators
- Xiaoqin Fu
- Contributors
- Haipeng Cai (Advisor)Zhe Dang (Committee Member)Dingwen Tao (Committee Member)
- Awarding Institution
- Washington State University
- Academic Unit
- Electrical Engineering and Computer Science, School of
- Theses and Dissertations
- Doctor of Philosophy (PhD), Washington State University
- Publisher
- Washington State University
- Number of pages
- 174
- Identifiers
- 99900898539501842
- Language
- English
- Resource Type
- Dissertation