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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 28 (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 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
DomainSpecific Optimization in Automata Learning
 In Proc. 15 th Int. Conf. on Computer Aided Verification
, 2003
"... Automatically generated models may provide the key towards controlling the evolution of complex systems, form the basis for test generation and may be applied as monitors for running applications. ..."
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Cited by 28 (2 self)
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Automatically generated models may provide the key towards controlling the evolution of complex systems, form the basis for test generation and may be applied as monitors for running applications.
Mutually enhancing test generation and specification inference
 In Proc. 3rd International Workshop on Formal Approaches to Testing of Software, volume 2931 of LNCS
, 2003
"... Abstract. Generating effective tests and inferring likely program specifications are both difficult and costly problems. We propose an approach in which we can mutually enhance the tests and specifications that are generated by iteratively applying each in a feedback loop. In particular, we infer li ..."
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Cited by 26 (3 self)
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Abstract. Generating effective tests and inferring likely program specifications are both difficult and costly problems. We propose an approach in which we can mutually enhance the tests and specifications that are generated by iteratively applying each in a feedback loop. In particular, we infer likely specifications from the executions of existing tests and use these specifications to guide automatic test generation. Then the existing tests, as well as the new tests, are used to infer new specifications in the subsequent iteration. The iterative process continues until there is no new test that violates specifications inferred in the previous iteration. Inferred specifications can guide test generation to focus on particular program behavior, reducing the scope of analysis; and newly generated tests can improve the inferred specifications. During each iteration, the generated tests that violate inferred specifications are collected to be inspected. These violating tests are likely to have a high probability of exposing faults or exercising new program behavior. Our hypothesis is that such a feedback loop can mutually enhance test generation and specification inference. 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 24 (7 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 24 (9 self)
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The following full text is a preprint version which may differ from the publisher's version.
Inferring canonical register automata
 in VMCAI
, 2012
"... Abstract. In this paper, we present an extension of active automata learning to register automata, an automaton model which is capable of expressing the influence of data on control flow. Register automata operate on an infinite data domain, whose values can be assigned to registers and compared fo ..."
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Abstract. In this paper, we present an extension of active automata learning to register automata, an automaton model which is capable of expressing the influence of data on control flow. Register automata operate on an infinite data domain, whose values can be assigned to registers and compared for equality. Our active learning algorithm is unique in that it directly infers the effect of data values on control flow as part of the learning process. This effect is expressed by means of registers and guarded transitions in the resulting register automata models. The application of our algorithm to a small example indicates the impact of learning register automata models: Not only are the inferred models much more expressive than finite state machines, but the prototype implementation also drastically outperforms the classic L * algorithm, even when exploiting optimal data abstraction and symmetry reduction.
Integration testing of components guided by incremental state machine learning
 In TAIC PART
, 2006
"... The design of complex systems, e.g., telecom services, is nowadays usually based on the integration of components (COTS), loosely coupled in distributed architectures. When components come from third party sources, their internal structure is usually unknown and the documentation is insufficient. Th ..."
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Cited by 14 (8 self)
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The design of complex systems, e.g., telecom services, is nowadays usually based on the integration of components (COTS), loosely coupled in distributed architectures. When components come from third party sources, their internal structure is usually unknown and the documentation is insufficient. Therefore, the system integrator faces the problem of providing a required system assembling COTS whose behaviour is barely specified and for which no model is usually available. In this paper, we address the problem of integration testing of COTS. It combines test generation techniques with machine learning algorithms. Statebased models of components are built from observed behaviours. The models are alternatively used to generate tests and extended to take into account observed behaviour. This process is iterated until a satisfactory level of confidence in testing is achieved. 1.
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.
Inference of eventrecording automata using timed decision trees
 Lecture Notes in Computer Science, 4137:435–449, 2006. In Proceedings of the 17th International Conference on Concurrency Theory
, 2006
"... In regular inference, the problem is to infer a regular language, typically represented by a deterministic finite automaton (DFA) from answers to a finite set of membership queries, each of which asks whether the language contains a certain word. There are many algorithms for learning DFAs, the most ..."
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Cited by 12 (3 self)
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In regular inference, the problem is to infer a regular language, typically represented by a deterministic finite automaton (DFA) from answers to a finite set of membership queries, each of which asks whether the language contains a certain word. There are many algorithms for learning DFAs, the most wellknown being the algorithm due to Dana Angluin. However, there are almost no extensions of these algorithms to the setting of timed systems. We present an algorithm for inferring a model of a timed system using Angluin’s setup. One of the most popular model for timed system is timed automata. Since timed automata can freely use an arbitrary number of clocks, we restrict our attention to systems that can be described by eventrecording automata (DERAs). In previous work, we have presented an algorithm for inferring a DERA in the form of a region graph. In this paper, we present a novel inference algorithm for DERAs, which avoids constructing a (usually prohibitively large) region graph. We must then develop techniques for inferring guards on transitions of a DERA. Our construction deviates from previous work on inference of DERAs in that it first constructs a so called timed decision tree from observations of system behavior, which is thereafter folded into an automaton. 1