Learning regular sets from queries and counterexamples
Information and Computation
MAS — an interactive synthesizer to support behavioral modelling in UML
ICSE '01 Proceedings of the 23rd International Conference on Software Engineering
Integration Testing of Components Guided by Incremental State Machine Learning
TAIC-PART '06 Proceedings of the Testing: Academic & Industrial Conference on Practice And Research Techniques
Regular inference for state machines with parameters
FASE'06 Proceedings of the 9th international conference on Fundamental Approaches to Software Engineering
Improving dynamic software analysis by applying grammar inference principles
Journal of Software Maintenance and Evolution: Research and Practice - Special Issue on Program Comprehension through Dynamic Analysis (PCODA)
Architecting Dependable Systems V
Regular inference for state machines using domains with equality tests
FASE'08/ETAPS'08 Proceedings of the Theory and practice of software, 11th international conference on Fundamental approaches to software engineering
Generating models of infinite-state communication protocols using regular inference with abstraction
ICTSS'10 Proceedings of the 22nd IFIP WG 6.1 international conference on Testing software and systems
Inferring compact models of communication protocol entities
ISoLA'10 Proceedings of the 4th international conference on Leveraging applications of formal methods, verification, and validation - Volume Part I
Learning and integration of parameterized components through testing
TestCom'07/FATES'07 Proceedings of the 19th IFIP TC6/WG6.1 international conference, and 7th international conference on Testing of Software and Communicating Systems
ISoLA'12 Proceedings of the 5th international conference on Leveraging Applications of Formal Methods, Verification and Validation: technologies for mastering change - Volume Part I
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The design of complex systems, e.g., telecom services, is usually based on the integration of components (COTS). When components come from third party sources, their internal structure is usually unknown and the documentation is scant or inadequate. Our work addresses the issue of providing a sound support to component integration in the absence of formal models. We consider components as black boxes and use an incremental learning approach to infer partial models. At the same time, we are focusing on the richer models that are more expressive in the designing of complex systems. Therefore, we propose an I/O parameterized model and an algorithm to infer it from a black box component. This is combined with interoperability testing covering models of the components.