Alexandre Petrenko
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Research Subjects
Alexandre Petrenko has a total of 93 co-authors. Here are the top co-authors by number of shared publications:
- Alberto Sangiovanni Vincentelli
University of California Berkeley
20 shared publications - Tiziano Villa
Dipartimento di Informatica Università degli Studi di Verona Verona Italy
20 shared publications - Alan Mishchenko
University of California, Berkeley
19 shared publications - Nina Yevtushenko
Tomsk State University
19 shared publications - Robert K. Brayton
University of California
19 shared publications - Adenilso Simao
University of São Paulo
16 shared publications
Latest Publications
- 23 September 2019
- Published by Springer Nature in Lecture Notes in Computer Science
- p. 112-128
Learning Communicating State Machines
- 23 September 2019
- Published by Springer Nature in Lecture Notes in Computer Science
- p. 112-128
Learning Communicating State Machines
- 9 September 2019
- Published by Springer Nature
- p. 243-256
Learning Minimal DFA: Taking Inspiration from RPNI to Improve SAT Approach
- 9 September 2019
- Published by Springer Nature
- p. 243-256
Learning Minimal DFA: Taking Inspiration from RPNI to Improve SAT Approach
- 1 July 2019
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 362-373
Learning and Adaptive Testing of Nondeterministic State Machines
The paper addresses the problems of active learning and conformance testing of systems modeled by nondeterministic Mealy machines (NFSM). It presents a unified SAT-based approach originally proposed by the authors for deterministic FSMs and now generalized to partial nondeterministic machines and checking experiments. Learning a nondeterministic black box, the approach neither needs a Teacher nor uses it a conformance tester to approximate equivalence queries. The idea behind this approach is to infer from a current set of traces not one, but two inequivalent conjectures, use an input sequence distinguishing them in an output query, and update the current trace set with an observed trace to obtain a new pair of distinguishable conjectures, if possible. The classical active learning problem is further generalized by adding a nondeterministic specification FSM, which defines the solution space. The setup unifies the learning and adaptive testing problems and makes them equisolvable with the proposed approach.
- 1 July 2019
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 362-373
Learning and Adaptive Testing of Nondeterministic State Machines
The paper addresses the problems of active learning and conformance testing of systems modeled by nondeterministic Mealy machines (NFSM). It presents a unified SAT-based approach originally proposed by the authors for deterministic FSMs and now generalized to partial nondeterministic machines and checking experiments. Learning a nondeterministic black box, the approach neither needs a Teacher nor uses it a conformance tester to approximate equivalence queries. The idea behind this approach is to infer from a current set of traces not one, but two inequivalent conjectures, use an input sequence distinguishing them in an output query, and update the current trace set with an observed trace to obtain a new pair of distinguishable conjectures, if possible. The classical active learning problem is further generalized by adding a nondeterministic specification FSM, which defines the solution space. The setup unifies the learning and adaptive testing problems and makes them equisolvable with the proposed approach.
- 1 April 2019
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 32-39
Using Imprecise Test Oracles Modelled by FSM
Test oracles are usually used to evaluate the behaviors of systems under test to reveal faults. In a typical conformance testing scenario, a test oracle is a deterministic finite state machine (FSM). However, uncertainty occurring in the design of an oracle may result in a set of potential candidate oracles which can compactly be represented by a nondeterministic FSM thus modelling an imprecise test oracle. In the context of testing deterministic systems, such an oracle should ideally be reduced to a precise, i.e., deterministic oracle. We elaborate two scenarios for dealing with imprecise test oracles that involve a domain expert playing the role of an "ultimate" oracle. In the first scenario, the expert chooses a right oracle by inspecting the generated tests differentiating all potential precise oracles which can be derived from a given imprecise one. In the second scenario, the expert evaluates tests one by one while they are generated, and the imprecise oracle is iteratively reduced until a single precise oracle remains, if at all.
- 1 April 2019
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 32-39
Using Imprecise Test Oracles Modelled by FSM
Test oracles are usually used to evaluate the behaviors of systems under test to reveal faults. In a typical conformance testing scenario, a test oracle is a deterministic finite state machine (FSM). However, uncertainty occurring in the design of an oracle may result in a set of potential candidate oracles which can compactly be represented by a nondeterministic FSM thus modelling an imprecise test oracle. In the context of testing deterministic systems, such an oracle should ideally be reduced to a precise, i.e., deterministic oracle. We elaborate two scenarios for dealing with imprecise test oracles that involve a domain expert playing the role of an "ultimate" oracle. In the first scenario, the expert chooses a right oracle by inspecting the generated tests differentiating all potential precise oracles which can be derived from a given imprecise one. In the second scenario, the expert evaluates tests one by one while they are generated, and the imprecise oracle is iteratively reduced until a single precise oracle remains, if at all.
- 29 January 2019
- Published by Springer Nature in Software Quality Journal
- Vol. 27 (2) , 501-527
Fault model-driven testing from FSM with symbolic inputs
- 29 January 2019
- Published by Springer Nature in Software Quality Journal
- Vol. 27 (2) , 501-527
Fault model-driven testing from FSM with symbolic inputs
- 13 December 2018
- Published by Springer Nature in Software Quality Journal
- Vol. 27 (2) , 651-674
FSM inference and checking sequence construction are two sides of the same coin
- 13 December 2018
- Published by Springer Nature in Software Quality Journal
- Vol. 27 (2) , 651-674
FSM inference and checking sequence construction are two sides of the same coin
- 15 October 2018
- Published by Springer Nature
- p. 354-375
Checking Sequence Generation for Symbolic Input/Output FSMs by Constraint Solving
- 15 October 2018
- Published by Springer Nature
- p. 354-375
Checking Sequence Generation for Symbolic Input/Output FSMs by Constraint Solving
- Open Access
- 14 October 2018
- Published by Association for Computing Machinery (ACM)
- p. 187
Towards Testing from Finite State Machines with Symbolic Inputs and Outputs
- 14 October 2018
- Published by Association for Computing Machinery (ACM)
- p. 187
Open AccessTowards Testing from Finite State Machines with Symbolic Inputs and Outputs
- 7 September 2018
- Published by Springer Nature
- p. 119-134
Conformance Testing and Inference of Embedded Components
- 7 September 2018
- Published by Springer Nature
- p. 119-134
Conformance Testing and Inference of Embedded Components