• Corpus ID: 16191377

Complementing Model Learning with Mutation-Based Fuzzing

@article{Smetsers2016ComplementingML,
  title={Complementing Model Learning with Mutation-Based Fuzzing},
  author={Rick Smetsers and Joshua Moerman and Mark Janssen and Sicco Verwer},
  journal={ArXiv},
  year={2016},
  volume={abs/1611.02429}
}
An ongoing challenge for learning algorithms formulated in the Minimally Adequate Teacher framework is to efficiently obtain counterexamples. In this paper we compare and combine conformance testing and mutation-based fuzzing methods for obtaining counterexamples when learning finite state machine models for the reactive software systems of the Rigorous Exampination of Reactive Systems (RERS) challenge. We have found that for the LTL problems of the challenge the fuzzer provided an independent… 

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References

SHOWING 1-10 OF 15 REFERENCES
The TTT Algorithm: A Redundancy-Free Approach to Active Automata Learning
TLDR
The distinguishing characteristic of TTT is its redundancy-free organization of observations, which can be exploited to achieve optimal (linear) space complexity, thanks to a thorough analysis of counterexamples, extracting and storing only the essential refining information.
Test Selection Based on Finite State Models
TLDR
A method for the selection of appropriate test case, an important issue for conformance testing of protocol implementations as well as software engineering, is presented and is shown to have general applicability, full fault-detection power, and yields shorter test suites than the W-method.
Diversity-based inference of finite automata
  • R. Rivest, R. Schapire
  • Computer Science
    28th Annual Symposium on Foundations of Computer Science (sfcs 1987)
  • 1987
TLDR
A new procedure for inferring the structure of a finitestate automaton (FSA) from its input/output behavior, using access to the automaton to perform experiments, based on the notion of equivalence between testa.
Next Generation LearnLib
The Next Generation LearnLib (NGLL) is a framework for model-based construction of dedicated learning solutions on the basis of extensible component libraries, which comprise various methods and
Learning Regular Sets from Queries and Counterexamples
Minimal Separating Sequences for All Pairs of States
TLDR
This paper presents an improved algorithm based on the minimization algorithm of Hopcroft that runs in \(\mathcal {O}(m \log n)\) time and the efficiency of this algorithm is empirically verified and compared to the traditional algorithm.
Formal Methods for Protocol Engineering and Distributed Systems
TLDR
This volume contains the proceedings of the Joint International Conference on Formal Description Techniques for Distributed Systems and Communication Protocols and Protocol Specification, Testing, and Verification, which was held in Beijing, China, in October 1999.
American Fuzzy Lop (AFL) fuzzer (2015), http://lcamtuf.coredump.cx/afl/, date
  • 2015
Failure diagnosis of automata
...
...