Learning regular sets from queries and counterexamples
Information and Computation
STATEMATE: A Working Environment for the Development of Complex Reactive Systems
IEEE Transactions on Software Engineering
An experiment in automatic generation of test suites for protocols with verification technology
Science of Computer Programming - Special issue on COST 247, verification and validation methods for formal descriptions
Bandera: extracting finite-state models from Java source code
Proceedings of the 22nd international conference on Software engineering
Model Generation by Moderated Regular Extrapolation
FASE '02 Proceedings of the 5th International Conference on Fundamental Approaches to Software Engineering
Logic Verification of ANSI-C Code with SPIN
Proceedings of the 7th International SPIN Workshop on SPIN Model Checking and Software Verification
TACAS '02 Proceedings of the 8th International Conference on Tools and Algorithms for the Construction and Analysis of Systems
ALT '01 Proceedings of the 12th International Conference on Algorithmic Learning Theory
Architecting Dependable Systems V
Compositional CSP Traces Refinement Checking
Electronic Notes in Theoretical Computer Science (ENTCS)
FM '09 Proceedings of the 2nd World Congress on Formal Methods
Heuristics for ioco-based test-based modelling
FMICS'06/PDMC'06 Proceedings of the 11th international workshop, FMICS 2006 and 5th international workshop, PDMC conference on Formal methods: Applications and technology
FMCO'06 Proceedings of the 5th international conference on Formal methods for components and objects
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Among other domains, learning finite-state machines is important for obtaining a model of a system under development, so that powerful formal methods such as model checking can be applied. A prominent algorithm for learning such devices was developed by Angluin. We have implemented this algorithm in a straightforward way to gain further insights to practical applicability. Furthermore, we have analyzed its performance on randomly generated as well as real-world examples. Our experiments focus on the impact of the alphabet size and the number of states on the needed number of membership queries. Additionally, we have implemented and analyzed an optimized version for learning prefix-closed regular languages. Memory consumption is one major obstacle when we attempted to learn large examples. We see that prefix-closed languages are relatively hard to learn compared to arbitrary regular languages. The optimization, however, shows positive results.