Validation with guided search of the state space
DAC '98 Proceedings of the 35th annual Design Automation Conference
Hints to accelerate Symbolic Traversal
CHARME '99 Proceedings of the 10th IFIP WG 10.5 Advanced Research Working Conference on Correct Hardware Design and Verification Methods
Error Detection with Directed Symbolic Model Checking
FM '99 Proceedings of the Wold Congress on Formal Methods in the Development of Computing Systems-Volume I - Volume I
KISS: keep it simple and sequential
Proceedings of the ACM SIGPLAN 2004 conference on Programming language design and implementation
Directed explicit-state model checking in the validation of communication protocols
International Journal on Software Tools for Technology Transfer (STTT)
Iterative context bounding for systematic testing of multithreaded programs
Proceedings of the 2007 ACM SIGPLAN conference on Programming language design and implementation
Going with the flow: parameterized verification using message flows
Proceedings of the 2008 International Conference on Formal Methods in Computer-Aided Design
A randomized scheduler with probabilistic guarantees of finding bugs
Proceedings of the fifteenth edition of ASPLOS on Architectural support for programming languages and operating systems
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We describe two new state exploration algorithms, called biased-dfs and biased-bfs, that bias the search towards regions more likely to have error states using high level hints supplied by the user. These hints are in the form of priorities or markings describing which transitions are important and which aren't. We will then describe a natural way to mark the transitions using flows or partial orders on system events. Apart from being easy to understand, flows express succinctly the basic organization of a system. An advantage of this approach is that assigning priorities does not involve low level details of the system. Using flow-derived priorities we study the performance of the biased algorithms in the context of cache coherence protocols by comparing them against standard bfs, dfs and directed model checking. Preliminary results are encouraging with biased-bfs finding bugs about 3 times faster on average than standard bfs while returning shortest counter examples almost always. Biased-dfs on the other hand is couple of orders of magnitude faster than bfs and slightly faster than even standard dfs while being more robust than it.