AALpy: an active automata learning library

Aarts F, Jonsson B, Uijen J (2010) Generating models of infinite-state communication protocols using regular inference with abstraction. In: ICTSS 2010

Aichernig BK, Tappler M (2019) Probabilistic black-box reachability checking (extended version). Formal Methods Syst Des 54(3):416–448

Aichernig BK, Mostowski W, Mousavi MR, Tappler M, Taromirad M (2018) Model learning and model-based testing. In: Bennaceur A, Hahnle R, Meinke K (eds) Machine Learning for Dynamic Software Analysis: Potentials and Limits - International Dagstuhl Seminar 16172, Dagstuhl Castle, Germany, April 24-27, 2016, Revised Papers, Springer, Lecture Notes in Computer Science, vol 11026, pp 74–100. https://doi.org/10.1007/978-3-319-96562-8_3

Aichernig BK, Tappler M, Wallner F (2020) Benchmarking combinations of learning and testing algorithms for active automata learning. TAP 2020:3–22

Aichernig BK, Muškardin E, Pferscher A (2021) Learning-based fuzzing of IoT message brokers. In: 14th IEEE Conference on Software Testing, Verification and Validation, ICST 2021, Porto de Galinhas, Brazil, April 12-16, 2021, IEEE, pp 47–58. https://doi.org/10.1109/ICST49551.2021.00017

Angluin D (1987) Learning regular sets from queries and counterexamples. Inf Comput 75(2):87–106

Castro J, Gavalda R (2016) Learning probability distributions generated by finite-state machines. In: Heinz J, Sempere JM (eds) Topics in Grammatical Inference, Springer, Berlin, Heidelberg, pp 113–142. https://doi.org/10.1007/978-3-662-48395-4_5

Chen Y, Nielsen TD (2012) Active learning of Markov decision processes for system verification. In: 11th International Conference on Machine Learning and Applications, ICMLA, Boca Raton, FL, USA, December 12-15, 2012. Volume 2, IEEE, pp 289–294. https://doi.org/10.1109/ICMLA.2012.158

Chollet F, et al. (2015) Keras. https://github.com/fchollet/keras

El-Fakih K, Groz R, Irfan MN, Shahbaz M (2010) Learning finite state models of observable nondeterministic systems in a testing context. ICTSS 2010:97–102

Fiterau-Brostean P, Janssen R, Vaandrager FW (2016) Combining model learning and model checking to analyze TCP implementations. In: Chaudhuri S, Farzan A (eds) Computer Aided Verification - 28th International Conference, CAV 2016, Toronto, ON, Canada, July 17-23, 2016, Proceedings, Part II, Springer, Lecture Notes in Computer Science, vol 9780, pp 454–471. https://doi.org/10.1007/978-3-319-41540-6_25

Fiterau-Brostean P, Lenaerts T, Poll E, de Ruiter J, Vaandrager FW, Verleg P (2017) Model learning and model checking of SSH implementations. In: Erdogmus H, Havelund K (eds) Proceedings of the 24th ACM SIGSOFT International SPIN Symposium on Model Checking of Software, Santa Barbara, CA, USA, July 10-14, 2017, ACM, pp 142–151. https://doi.org/10.1145/3092282.3092289

Fiterau-Brostean P, Jonsson B, Merget R, de Ruiter J, Sagonas K, Somorovsky J (2020) Analysis of DTLS implementations using protocol state fuzzing. In: Capkun S, Roesner F (eds) 29th USENIX Security Symposium, USENIX Security 2020, August 12-14, 2020, USENIX Association, pp 2523–2540. https://www.usenix.org/conference/usenixsecurity20/presentation/fiterau-brostean

Groz R, Bremond N, Simao A, Oriat C (2020) hW-inference: A heuristic approach to retrieve models through black box testing. JSS 159:110426

Heule MJH, Verwer S (2010) Exact DFA identification using SAT solvers. In: Sempere JM, Garcia P (eds) ICGI 2010, pp 66–79

Howar F, Steffen B, Merten M (2010) From ZULU to RERS – lessons learned in the ZULU challenge. In: ISoLA 2010, LNCS, vol 6415, pp 687–704

Hungar H, Niese O, Steffen B (2003) Domain-specific optimization in automata learning. In: Jr WAH, Somenzi F (eds) Computer Aided Verification, 15th International Conference, CAV 2003, Boulder, CO, USA, July 8-12, 2003, Proceedings, Springer, Lecture Notes in Computer Science, vol 2725, pp 315–327. https://doi.org/10.1007/978-3-540-45069-6_31

Isberner M, Howar F, Steffen B (2015) The open-source LearnLib – a framework for active automata learning. In: CAV 2015 (I), LNCS, vol 9206, pp 487–495

Kwiatkowska MZ, Norman G, Parker D (2011) PRISM 4.0: Verification of probabilistic real-time systems. In: CAV 2011, LNCS, vol 6806, pp 585–591

Mao H, Chen Y, Jaeger M, Nielsen TD, Larsen KG, Nielsen B (2016) Learning deterministic probabilistic automata from a model checking perspective. Mach Learn 105(2):255–299

Mohri M, Rostamizadeh A, Talwalkar A (2012) Foundations of Machine Learning. Adaptive computation and machine learning, MIT Press. http://mitpress.mit.edu/books/foundations-machine-learning-0

Muskardin E, Aichernig BK, Pill I, Pferscher A, Tappler M (2021) Aalpy: An active automata learning library. In: Hou Z, Ganesh V (eds) Automated Technology for Verification and Analysis - 19th International Symposium, ATVA 2021, Gold Coast, QLD, Australia, October 18-22, 2021, Proceedings, Springer, Lecture Notes in Computer Science, vol 12971, pp 67–73. https://doi.org/10.1007/978-3-030-88885-5_5

Muškardin E, Pill I, Tappler M, Aichernig BK (2021) Automata learning enabling model-based diagnosis. In: 32nd International Workshop on Principle of Diagnosis, Hamburg-Germany, September 13th-15th

Neider D, Smetsers R, Vaandrager F, Kuppens H (2019) Benchmarks for automata learning and conformance testing. In: Models, Mindsets, Meta: The What, the How, and the Why Not?, LNCS, vol 11200, pp 390–416

Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, Desmaison A, Kopf A, Yang E, DeVito Z, Raison M, Tejani A, Chilamkurthy S, Steiner B, Fang L, Bai J, Chintala S (2019) Pytorch: An imperative style, high-performance deep learning library. In: Wallach HM, Larochelle H, Beygelzimer A, d’Alche-Buc F, Fox EB, Garnett R (eds) Advances in Neural Information Processing Systems 32, Curran Associates, Inc., pp 8024–8035

Peled DA, Vardi MY, Yannakakis M (2002) Black box checking. J Autom Lang Comb 7(2):225–246

Pferscher A, Aichernig BK (2020) Learning abstracted non-deterministic finite state machines. In: Casola V, Benedictis AD, Rak M (eds) Testing Software and Systems - 32nd IFIP WG 6.1 International Conference, ICTSS 2020, Naples, Italy, December 9-11, 2020, Proceedings, Springer, Lecture Notes in Computer Science, vol 12543, pp 52–69. https://doi.org/10.1007/978-3-030-64881-7_4

Pferscher A, Aichernig BK (2021) Fingerprinting Bluetooth Low Energy devices via active automata learning. In: Formal Methods - 24th International Symposium, FM 2021, Beijing, China, November 20-26, 2021, Accepted, Springer

Rivest RL, Schapire RE (1993) Inference of finite automata using homing sequences. Inform. Comput. 103(2):299–347

de Ruiter J, Poll E (2015) Protocol state fuzzing of TLS implementations. In: Jung J, Holz T (eds) 24th USENIX Security Symposium, USENIX Security 15, Washington, D.C., USA, August 12-14, 2015, USENIX Association, pp 193–206. https://www.usenix.org/conference/usenixsecurity15/technical-sessions/presentation/de-ruiter

Scapy (2021) Scapy. https://github.com/secdev/scapy/, Accessed Sept 10 2021

Shahbaz M, Groz R (2009) Inferring Mealy machines. In: FM 2009, LNCS, vol 5850, pp 207–222

Stone CM, Chothia T, de Ruiter J (2018) Extending automated protocol state learning for the 802.11 4-way handshake. In: opez J, Zhou J, Soriano M (eds) Computer Security - 23rd European Symposium on Research in Computer Security, ESORICS 2018, Barcelona, Spain, September 3-7, 2018, Proceedings, Part I, Springer, Lecture Notes in Computer Science, vol 11098, pp 325–345. https://doi.org/10.1007/978-3-319-99073-6_16

Tappler M (2019) Evaluation material for $${L}^*$$-based learning of Markov decision processes (37). Available via https://doi.org/10.6084/m9.figshare.7960928.v1, Accessed Sept 10 2021

Tappler M, Aichernig BK, Bloem R (2017) Model-based testing IoT communication via active automata learning. In: 2017 IEEE International Conference on Software Testing, Verification and Validation, ICST 2017, Tokyo, Japan, March 13-17, 2017, IEEE Computer Society, pp 276–287. https://doi.org/10.1109/ICST.2017.32

Tappler M, Aichernig BK, Bacci G, Eichlseder M, Larsen KG (2019a) L*-based learning of Markov decision processes. In: ter Beek MH, McIver A, Oliveira JN (eds) Formal Methods - The Next 30 Years - Third World Congress, FM 2019, Porto, Portugal, October 7-11, 2019, Proceedings, Springer, Lecture Notes in Computer Science, vol 11800, pp 651–669. https://doi.org/10.1007/978-3-030-30942-8_38

Tappler M, Aichernig BK, Bacci G, Eichlseder M, Larsen KG (2019b) $${L}^*$$-based learning of Markov decision processes. In: FM 2019, LNCS, vol 11800, pp 651–669

Tappler M, Aichernig BK, Bacci G, Eichlseder M, Larsen KG (2021a) L*-based learning of Markov decision processes (extended version). FAOC

Tappler M, Muškardin E, Aichernig BK, Pill I (2021b) Active learning of stochastic reactive systems. In: Software Engineering and Formal Methods - 19th International Conference, SEFM 2021, Lecture Notes in Computer Science

Tiobe (2018) https://www.tiobe.com/tiobe-index/, Accessed Sept 10 2021