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27
Regular inference for state machines with parameters
 In FASE
, 2006
"... Abstract. Techniques for inferring a regular language, in the form of a finite automaton, from a sufficiently large sample of accepted and nonaccepted input words, have been employed to construct models of software and hardware systems, for use, e.g., in test case generation. We intend to adapt thes ..."
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Abstract. Techniques for inferring a regular language, in the form of a finite automaton, from a sufficiently large sample of accepted and nonaccepted input words, have been employed to construct models of software and hardware systems, for use, e.g., in test case generation. We intend to adapt these techniques to construct state machine models of entities of communication protocols. The alphabet of such state machines can be very large, since a symbol typically consists of a protocol data unit type with a number of parameters, each of which can assume many values. In typical algorithms for regular inference, the number of needed input words grows with the size of the alphabet and the size of the minimal DFA accepting the language. We therefore modify such an algorithm (Angluin’s algorithm) so that its complexity grows not with the size of the alphabet, but only with the size of a certain symbolic representation of the DFA. The main new idea is to infer, for each state, a partitioning of input symbols into equivalence classes, under the hypothesis that all input symbols in an equivalence class have the same effect on the state machine. Whenever such a hypothesis is disproved, equivalence classes are refined. We show that our modification retains the good properties of Angluin’s original algorithm, but that its complexity grows with the size of our symbolic DFA representation rather than with the size of the alphabet. We have implemented the algorithm; experiments on synthesized examples are consistent with these complexity results. 1
On the correspondence between conformance testing and regular inference
 of Lecture Notes in Computer Science
, 2005
"... Abstract. Conformance testing for finite state machines and regular inference both aim at identifying the model structure underlying a black box system on the basis of a limited set of observations. Whereas the former technique checks for equivalence with a given conjecture model, the latter techniq ..."
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Cited by 26 (12 self)
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Abstract. Conformance testing for finite state machines and regular inference both aim at identifying the model structure underlying a black box system on the basis of a limited set of observations. Whereas the former technique checks for equivalence with a given conjecture model, the latter techniques addresses the corresponding synthesis problem by means of techniques adopted from automata learning. In this paper we establish a common framework to investigate the similarities of these techniques by showing how results in one area can be transferred to results in the other and to explain the reasons for their differences. 1
Regular inference for state machines using domains with equality tests
 In Fundamental Approaches to Software Engineering
, 2008
"... Abstract. Existing algorithms for regular inference (aka automata learning) allows to infer a finite state machine by observing the output that the machine produces in response to a selected sequence of input strings. We generalize regular inference techniques to infer a class of state machines wi ..."
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Cited by 23 (6 self)
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Abstract. Existing algorithms for regular inference (aka automata learning) allows to infer a finite state machine by observing the output that the machine produces in response to a selected sequence of input strings. We generalize regular inference techniques to infer a class of state machines with an infinite state space. We consider Mealy machines extended with state variables that can assume values from a potentially unbounded domain. These values can be passed as parameters in input and output symbols, and can be used in tests for equality between state variables and/or message parameters. This is to our knowledge the first extension of regular inference to infinitestate systems. We intend to use these techniques to generate models of communication protocols from observations of their inputoutput behavior. Such protocols often have parameters that represent node adresses, connection identifiers, etc. that have a large domain, and on which test for equality is the only meaningful operation. Our extension consists of two phases. In the first phase we apply an existing inference technique for finitestate Mealy machines to generate a model for the case that the values are taken from a small data domain. In the second phase we transform this finitestate Mealy machine into an infinitestate Mealy machine by folding it into a compact symbolic form. 1
Generating models of infinitestate communication protocols using regular inference with abstraction
 22nd IFIP International Conference on Testing Software and Systems
"... The following full text is a preprint version which may differ from the publisher's version. ..."
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Cited by 23 (8 self)
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The following full text is a preprint version which may differ from the publisher's version.
Reverse Engineering State Machines by Interactive Grammar Inference
 In 14th IEEE International Working Conference on Reverse Engineering (WCRE
, 2007
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Learning communicating automata from MSCs
 IEEE Transactions on Software Engineering
, 2010
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Insights to Angluin's Learning
 In International Workshop on Software Verification and Validation (SVV
, 2003
"... Among other domains, learning finitestate machines is important for obtaining a model of a system under development, so that powerful formal methods such as model checking can be applied. ..."
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Cited by 13 (3 self)
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Among other domains, learning finitestate machines is important for obtaining a model of a system under development, so that powerful formal methods such as model checking can be applied.
Replaying play in and play out: Synthesis of design models from scenarios by learning
 Proceedings of the 13 th International Conference on Tools and Algorithms for Construction and Analysis of Systems (TACAS’07
, 2007
"... Abstract. This paper is concerned with bridging the gap between requirements, provided as a set of scenarios, and conforming design models. The novel aspect of our approach is to exploit learning for the synthesis of design models. In particular, we present a procedure that infers a messagepassing ..."
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Abstract. This paper is concerned with bridging the gap between requirements, provided as a set of scenarios, and conforming design models. The novel aspect of our approach is to exploit learning for the synthesis of design models. In particular, we present a procedure that infers a messagepassing automaton (MPA) from a given set of positive and negative scenarios of the system’s behavior provided as message sequence charts (MSCs). The paper investigates which classes of regular MSC languages and corresponding MPA can (not) be learned, and presents a dedicated tool based on the learning library LearnLib that supports our approach. 1
Learning and integration of parameterized components through testing
 In TestCom/FATES
, 2007
"... Abstract. We investigate the use of parameterized state machine models to drive integration testing, in the case where the models of components are not available beforehand. Therefore, observations from tests are used to learn partial models of components, from which further tests can be derived for ..."
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Abstract. We investigate the use of parameterized state machine models to drive integration testing, in the case where the models of components are not available beforehand. Therefore, observations from tests are used to learn partial models of components, from which further tests can be derived for integration. We have extended previous algorithms to the case of finite state models with predicates on input parameters and observable nondeterminism. We also propose a new strategy where integration tests can be derived from the data collected during the learning process. Our work typically addresses the problem of assembling telecommunication services from black box COTS. 1
Learning I/O Automata
"... Links are established between three widely used modeling frameworks for reactive systems: the ioco theory of Tretmans, the interface automata of De Alfaro and Henzinger, and Mealy machines. It is shown that, by exploiting these links, any tool for active learning of Mealy machines can be used for l ..."
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Cited by 13 (6 self)
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Links are established between three widely used modeling frameworks for reactive systems: the ioco theory of Tretmans, the interface automata of De Alfaro and Henzinger, and Mealy machines. It is shown that, by exploiting these links, any tool for active learning of Mealy machines can be used for learning I/O automata that are deterministic and output determined. The main idea is to place a transducer in between the I/O automata teacher and the Mealy machine learner, which translates concepts from the world of I/O automata to the world of Mealy machines, and vice versa. The transducer comes equipped with an interface automaton that allows us to focus the learning process on those parts of the behavior that can effectively be tested and/or are of particular interest. The approach has been implemented on top of the LearnLib tool and has been applied successfully to three case studies.