Universal automata and NFA learning

Garcia, Pedro Castillo; Parga, Manuel Vázquez de; Alvarez, Gloria; Ruiz, José

doi:10.1016/j.tcs.2008.05.017

Cited by 20 publications

(3 citation statements)

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“…Como se verá en esta sección, es posible obtener convergencia en el límite independientemente del orden en que se mezclan los estados de una máquina no determinista [GdPAR07]. El argumento es el siguiente: dada una muestra universal es posible agrupar y reagrupar las cadenas que la componen, de modo que las muestras negativas sean suficientes para garantizar que cualquier partición de los estados de AM (D + ) produce un autómata que reconoce el lenguaje objetivo L.…”

Section: Convergencia De La Mezcla De Estados En Máquinas No Determinunclassified

“…El estudio de la convergencia cobró particular interés en esta investigación y permitió concluir que un algoritmo de mezcla de estados converge en el límite independientemente del orden en que se realicen las mezclas, tanto en el caso de inferir DFAs como NFAs [GdPAR07]. Este resultado es importante, porque muestra que la técnica EDSM (Evidence Driven State Merging) es convergente y por lo tanto lo son los algoritmos que la utilizan.…”

Section: Conclusionesunclassified

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Estudio de la mezcla de estados determinista y no determinista en el diseño de algoritmos para inferencia gramatical de lenguajes regulares.

Vargas¹,

Inés²

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Section: Convergencia De La Mezcla De Estados En Máquinas No Determinunclassified

Section: Conclusionesunclassified

Estudio de la mezcla de estados determinista y no determinista en el diseño de algoritmos para inferencia gramatical de lenguajes regulares.

Vargas¹,

Inés²

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“…Garcia et al [GdPAR08] present a state-merging algorithm that generates a nondeterministic nite automaton. Their algorithm is independent of the order of state-merging, provided that a universal sample is available that identi es the state partitions.…”

Section: Process Miningmentioning

confidence: 99%

Learning Finite State Machine Specifications from Test Cases

Werner¹

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Up-to-date software systems are often modular and need to be changeable. While modularity is believed to reduce software production costs, it also leads to increased di culty in testing, as the future uses of a module are getting more varied and therefore harder to guess. Also, reused modules are often represented as black boxes, whose interior structure is hidden from the system. In consequence, on reusing a module, the testing focuses on integration testing and monitoring the interfaces of the module.In order to monitor a system, a model of the system is needed to compare the observed traces to. However, with the advent of agile development methods and decreasing time to market, formal models of a system are seldom available. While formal modeling has not been widely adopted in industry, the importance of testing has increased, in part thanks to the same agile development methods that obsolete explicit modeling. An example is the test rst paradigm of eXtreme Programming, which requires that the tests for any software have to be written before the software itself. Therefore, test cases are available even for systems without a formal model.In consequence, we propose to generate a system model suitable for monitoring from a test suite for the system. The approach is based on automata learning. Angluin's learning algorithm is used to generate an appropriate model, while state-merging methods are applied to represent the test cases in a format that can be processed by the learning algorithm.Both Angluin's algorithm and state-merging are tailored to the characteristics of testing. For Angluin's algorithm, this comprises a mapping of the query mechanisms onto a test suite. The state-merging is used to construct a generic representation of arbitrary test suites by exploiting the properties of a given test speci cation language for a better coverage. The approach is implemented in a prototypical tool and validated by a case study.Like Alice in Wonderland, the research for this thesis has taken me to various unknown shores and topics. Many friends and colleagues have accompanied me along the path of my scienti c journey, and to all of them I o er my thanks for their company.First and foremost, I want to give my thanks to my supervisor, Prof. Dr. Jens Grabowski. Without his patience and persistence, this thesis would never have been completed.I am also very grateful to all colleagues who o ered their advice on this thesis. Franz Schenk was the rst one to read my initial e orts, and his comments on structure have greatly eased my task. Ste en Herbold proved to be the person with the most insight on the mathematical foundations of my research topic; therefore he was the one to spot any theoretical inconsistencies. Philip Makedonski has helped to polish my English phrasing. Special thanks are due to Prof. Dr. Wolfgang May for his valuable comments on correctly formulating mathematical de nitions.Many grateful thoughts also go to all my friends and family, whose constant support and never ending inquiries as to the progress o...

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A Comparison of Selected Variable Ordering Methods for NFA Induction

Jastrząb

2019

Lecture Notes in Computer Science

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Universal automata and NFA learning

Cited by 20 publications

References 3 publications

Estudio de la mezcla de estados determinista y no determinista en el diseño de algoritmos para inferencia gramatical de lenguajes regulares.

Estudio de la mezcla de estados determinista y no determinista en el diseño de algoritmos para inferencia gramatical de lenguajes regulares.

Learning Finite State Machine Specifications from Test Cases

A Comparison of Selected Variable Ordering Methods for NFA Induction

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