Markus Kusano
Virginia Tech
14 Papers
83 Citations
Markus Kusano is an academic researcher from Virginia Tech. The author has contributed to research in topics: Concurrency & Datalog. The author has an hindex of 11, co-authored 14 publications.
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Papers
Conc-iSE: incremental symbolic execution of concurrent software
Shengjian Guo,Markus Kusano,Chao Wang +2 more
- 25 Aug 2016
TL;DR: This paper develops an inter-thread and inter-procedural change-impact analysis to check if a statement is affected by the changes and then leverage the information to choose executions that need to be re-explored.
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Static DOM event dependency analysis for testing web applications
Chungha Sung,Markus Kusano,Nishant Sinha,Chao Wang +3 more
- 01 Nov 2016
TL;DR: This work proposes the first constraint-based declarative program analysis procedure for computing dependencies over program variables as well as event-handler functions of the various DOM elements, which is crucial for analyzing the behavior of a client-side web application.
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Assertion guided abstraction: a cooperative optimization for dynamic partial order reduction
Markus Kusano,Chao Wang +1 more
- 15 Sep 2014
TL;DR: A new method is developed, called assertion guided abstraction, which leverages both static and dynamic program analyses in a cooperative framework to reduce the interleaving space during stateless model checking of multithreaded C/C++ programs.
Modular verification of interrupt-driven software
Chungha Sung,Markus Kusano,Chao Wang +2 more
- 30 Oct 2017
TL;DR: In this article, an abstract interpretation framework for static verification of interrupt-driven software is proposed, which analyzes each interrupt handler in isolation as if it were a sequential program, and then propagates the result to other interrupt handlers.
18
•Posted Content
Thread-Modular Static Analysis for Relaxed Memory Models
Markus Kusano,Chao Wang +1 more
TL;DR: In this article, a unified framework for deciding the feasibility of inter-thread interferences is proposed to avoid propagating spurious data flows during static analysis and thus boost the performance of the static analyzer.
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