Exploring very large state spaces using genetic algorithms
International Journal on Software Tools for Technology Transfer (STTT) - Special section on tools and algorithms for the construction and analysis of systems
Applications of synchronization coverage
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Searching for liveness property violations in concurrent systems with ACO
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Finding deadlocks in large concurrent java programs using genetic algorithms
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Noise Makers Need to Know Where to be Silent - Producing Schedules That Find Bugs
ISOLA '06 Proceedings of the Second International Symposium on Leveraging Applications of Formal Methods, Verification and Validation
Concurrent Java Test Generation as a Search Problem
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Metaheuristics: From Design to Implementation
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A platform for search-based testing of concurrent software
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dBug: systematic testing of unmodified distributed and multi-threaded systems
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Noise injection heuristics for concurrency testing
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Coverage metrics for saturation-based and search-based testing of concurrent software
RV'11 Proceedings of the Second international conference on Runtime verification
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Noise injection disturbs the scheduling of program threads in order to increase the probability that more of their different legal interleavings occur during the testing process. However, there exist many different types of noise heuristics with many different parameters that are not easy to set such that noise injection is really efficient. In this paper, we propose a new way of using genetic algorithms to search for suitable types of noise heuristics and their parameters. This task is formalized as the test and noise configuration search problem in the paper, followed by a discussion of how to represent instances of this problem for genetic algorithms, which objectives functions to use, as well as parameter tuning of genetic algorithms when solving the problem. The proposed approach is evaluated on a set of benchmarks, showing that it provides significantly better results than the so far preferred random noise injection.