{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T20:16:24Z","timestamp":1784837784091,"version":"3.55.0"},"publisher-location":"Cham","reference-count":53,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031572487","type":"print"},{"value":"9783031572494","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,4,5]],"date-time":"2024-04-05T00:00:00Z","timestamp":1712275200000},"content-version":"vor","delay-in-days":95,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Existing active automata learning (AAL) algorithms have demonstrated their potential in capturing the behavior of complex systems (e.g., in analyzing network protocol implementations). The most widely used AAL algorithms generate finite state machine models, such as Mealy machines. For many analysis tasks, however, it is crucial to generate richer classes of models that also show how relations between data parameters affect system behavior. Such models have shown potential to uncover critical bugs, but their learning algorithms do not scale beyond small and well curated experiments. In this paper, we present <jats:inline-formula><jats:alternatives><jats:tex-math>$${SL}^{\\lambda }$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:msup>\n                    <mml:mrow>\n                      <mml:mi>SL<\/mml:mi>\n                    <\/mml:mrow>\n                    <mml:mi>\u03bb<\/mml:mi>\n                  <\/mml:msup>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula>, an effective and scalable register automata (RA) learning algorithm that significantly reduces the number of tests required for inferring models. It achieves this by combining a tree-based cost-efficient data structure with mechanisms for computing short and restricted tests. We have implemented <jats:inline-formula><jats:alternatives><jats:tex-math>$${SL}^{\\lambda }$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:msup>\n                    <mml:mrow>\n                      <mml:mi>SL<\/mml:mi>\n                    <\/mml:mrow>\n                    <mml:mi>\u03bb<\/mml:mi>\n                  <\/mml:msup>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> as a new algorithm in RALib. We evaluate its performance by comparing it against <jats:inline-formula><jats:alternatives><jats:tex-math>$${SL}^{*}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:msup>\n                    <mml:mrow>\n                      <mml:mi>SL<\/mml:mi>\n                    <\/mml:mrow>\n                    <mml:mrow>\n                      <mml:mrow\/>\n                      <mml:mo>\u2217<\/mml:mo>\n                    <\/mml:mrow>\n                  <\/mml:msup>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula>, the current state-of-the-art RA learning algorithm, in a series of experiments, and show superior performance and substantial asymptotic improvements in bigger systems.<\/jats:p>","DOI":"10.1007\/978-3-031-57249-4_5","type":"book-chapter","created":{"date-parts":[[2024,4,4]],"date-time":"2024-04-04T07:02:35Z","timestamp":1712214155000},"page":"87-108","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Scalable Tree-based Register Automata Learning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9730-9335","authenticated-orcid":false,"given":"Simon","family":"Dierl","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5185-0035","authenticated-orcid":false,"given":"Paul","family":"Fiterau-Brostean","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9524-4459","authenticated-orcid":false,"given":"Falk","family":"Howar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7897-601X","authenticated-orcid":false,"given":"Bengt","family":"Jonsson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9657-0179","authenticated-orcid":false,"given":"Konstantinos","family":"Sagonas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4066-9078","authenticated-orcid":false,"given":"Fredrik","family":"T\u00e5quist","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,4,5]]},"reference":[{"key":"5_CR1","doi-asserted-by":"publisher","unstructured":"Aarts, F., Jonsson, B., Uijen, J., Vaandrager, F.: Generating models of infinite-state communication protocols using regular inference with abstraction. Formal Methods in System Design pp. 1\u201341 (2015). https:\/\/doi.org\/10.1007\/s10703-014-0216-x","DOI":"10.1007\/s10703-014-0216-x"},{"key":"5_CR2","doi-asserted-by":"publisher","unstructured":"Aarts, F., Fiterau-Brostean, P., Kuppens, H., Vaandrager, F.: Learning register automata with fresh value generation. In: Leucker, M., Rueda, C., Valencia, F.D. (eds.) Theoretical Aspects of Computing - ICTAC 2015. LNCS, vol.\u00a09399, pp. 165\u2013183. Springer International Publishing, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-25150-9_11","DOI":"10.1007\/978-3-319-25150-9_11"},{"key":"5_CR3","doi-asserted-by":"publisher","unstructured":"Aarts, F., Heidarian, F., Kuppens, H., Olsen, P., Vaandrager, F.: Automata learning through counterexample guided abstraction refinement. In: Giannakopoulou, D., M\u00e9ry, D. (eds.) FM 2012: Formal Methods. LNCS, vol.\u00a07436, pp. 10\u201327. Springer, Berlin, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-32759-9_4","DOI":"10.1007\/978-3-642-32759-9_4"},{"key":"5_CR4","doi-asserted-by":"publisher","unstructured":"Aarts, F., Jonsson, B., Uijen, J., Vaandrager, F.: Generating models of infinite-state communication protocols using regular inference with abstraction. Formal Methods in System Design 46(1), 1\u201341 (Feb 2015). https:\/\/doi.org\/10.1007\/s10703-014-0216-x","DOI":"10.1007\/s10703-014-0216-x"},{"key":"5_CR5","unstructured":"Aarts, F., Kuppens, H., Tretmans, J., Vaandrager, F.W., Verwer, S.: Learning and testing the bounded retransmission protocol. In: Proceedings of the Eleventh International Conference on Grammatical Inference, ICGI 2012. JMLR Proceedings, vol.\u00a021, pp. 4\u201318. JMLR.org (2012),http:\/\/proceedings.mlr.press\/v21\/aarts12a.html"},{"key":"5_CR6","doi-asserted-by":"publisher","unstructured":"Ammons, G., Bod\u00edk, R., Larus, J.R.: Mining specifications. In: Proc. 29$$^{th}$$ ACM Symp. on Principles of Programming Languages. pp. 4\u201316. ACM (2002). https:\/\/doi.org\/10.1145\/503272.503275","DOI":"10.1145\/503272.503275"},{"key":"5_CR7","doi-asserted-by":"publisher","unstructured":"Angluin, D.: Learning regular sets from queries and counterexamples. Information and Computation 75(2), 87\u2013106 (1987). https:\/\/doi.org\/10.1016\/0890-5401(87)90052-6","DOI":"10.1016\/0890-5401(87)90052-6"},{"key":"5_CR8","doi-asserted-by":"publisher","unstructured":"Bollig, B., Habermehl, P., Leucker, M., Monmege, B.: A fresh approach to learning register automata. In: Developments in Language Theory. LNCS, vol.\u00a07907, pp. 118\u2013130. Springer Verlag (2013). https:\/\/doi.org\/10.1007\/978-3-642-38771-5_12","DOI":"10.1007\/978-3-642-38771-5_12"},{"key":"5_CR9","unstructured":"Cassel, S., Howar, F., Jonsson, B.: RALib: a LearnLib extension for inferring EFSMs. In: Proceedings of the 4th International Workshop on Design and Implementation of Formal Tools and Systems (DIFTS). pp.\u00a01\u20138 (2015), https:\/\/www.faculty.ece.vt.edu\/chaowang\/difts2015\/papers\/paper_5.pdf"},{"key":"5_CR10","doi-asserted-by":"publisher","unstructured":"Cassel, S., Howar, F., Jonsson, B., Steffen, B.: Active learning for extended finite state machines. Formal Asp. Comput. 28(2), 233\u2013263 (2016). https:\/\/doi.org\/10.1007\/s00165-016-0355-5","DOI":"10.1007\/s00165-016-0355-5"},{"key":"5_CR11","doi-asserted-by":"publisher","unstructured":"Champarnaud, J.M., Parantho\u00ebn, T.: Random generation of DFAs. Theoretical Computer Science 330(2), 221\u2013235 (Feb 2005). https:\/\/doi.org\/10.1016\/j.tcs.2004.03.072","DOI":"10.1016\/j.tcs.2004.03.072"},{"key":"5_CR12","doi-asserted-by":"publisher","unstructured":"Dierl, S., Fiterau-Brostean, P., Howar, F., Jonsson, B., Sagonas, K., T\u00e5quist, F.: Scalable tree-based register automata learning. arXiv CoRR (Jan 2024). https:\/\/doi.org\/10.48550\/arXiv.2401.14324, Extended version of the TACAS 2024 paper.","DOI":"10.48550\/arXiv.2401.14324"},{"key":"5_CR13","doi-asserted-by":"publisher","unstructured":"Drews, S., D\u2019Antoni, L.: Learning symbolic automata. In: Legay, A., Margaria, T. (eds.) Tools and Algorithms for the Construction and Analysis of Systems. LNCS, vol. 10205, pp. 173\u2013189. Springer, Berlin, Heidelberg (2017). https:\/\/doi.org\/10.1007\/978-3-662-54577-5_10","DOI":"10.1007\/978-3-662-54577-5_10"},{"key":"5_CR14","doi-asserted-by":"publisher","unstructured":"Esparza, J., Leucker, M., Schlund, M.: Learning workflow Petri nets. Fundamenta Informaticae 113(3-4), 205\u2013228 (2011). https:\/\/doi.org\/10.3233\/FI-2011-607","DOI":"10.3233\/FI-2011-607"},{"key":"5_CR15","doi-asserted-by":"publisher","unstructured":"Ferreira, T., Brewton, H., D\u2019Antoni, L., Silva, A.: Prognosis: Closed-box analysis of network protocol implementations. In: ACM SIGCOMM 2021 Conference. pp. 762\u2013774. ACM (Aug 2021). https:\/\/doi.org\/10.1145\/3452296.3472938","DOI":"10.1145\/3452296.3472938"},{"key":"5_CR16","doi-asserted-by":"publisher","unstructured":"Fiterau-Brostean, P., Howar, F.: Learning-based testing the sliding window behavior of TCP implementations. In: Critical Systems: Formal Methods and Automated Verification - Joint 22nd International Workshop on Formal Methods for Industrial Critical Systems - and - 17th International Workshop on Automated Verification of Critical Systems, FMICS-AVoCS. LNCS, vol. 10471, pp. 185\u2013200. Springer (2017). https:\/\/doi.org\/10.1007\/978-3-319-67113-0_12","DOI":"10.1007\/978-3-319-67113-0_12"},{"key":"5_CR17","doi-asserted-by":"publisher","unstructured":"Fiterau-Brostean, P., Jonsson, B., Sagonas, K., T\u00e5quist, F.: DTLS-Fuzzer: A DTLS protocol state fuzzer. In: 15th IEEE Conference on Software Testing, Verification and Validation. pp. 456\u2013458. ICST 2022, IEEE (Apr 2022). https:\/\/doi.org\/10.1109\/ICST53961.2022.00051","DOI":"10.1109\/ICST53961.2022.00051"},{"key":"5_CR18","doi-asserted-by":"crossref","unstructured":"Fiterau-Brostean, P., Jonsson, B., Sagonas, K., T\u00e5quist, F.: Automata-based automated detection of state machine bugs in protocol implementations. In: Network and Distributed System Security Symposium. NDSS 2023, The Internet Society (Feb 2023), https:\/\/www.ndss-symposium.org\/wp-content\/uploads\/2023\/02\/ndss2023_s68_paper.pdf","DOI":"10.14722\/ndss.2023.23068"},{"key":"5_CR19","unstructured":"Fiter\u0103u-Bro\u015ftean, P., Jonsson, B., Merget, R., de\u00a0Ruiter, J., Sagonas, K., Somorovsky, J.: Analysis of DTLS implementations using protocol state fuzzing. In: 29th USENIX Security Symposium (USENIX Security 20). pp. 2523\u20132540. USENIX Association (Aug 2020), https:\/\/www.usenix.org\/conference\/usenixsecurity20\/presentation\/fiterau-brostean"},{"key":"5_CR20","doi-asserted-by":"publisher","unstructured":"Fiter\u0103u-Bro\u015ftean, P., Janssen, R., Vaandrager, F.: Combining model learning and model checking to analyze TCP implementations. In: Chaudhuri, S., Farzan, A. (eds.) Computer Aided Verification. LNCS, vol.\u00a09780, pp. 454\u2013471. Springer International Publishing, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-41540-6_25","DOI":"10.1007\/978-3-319-41540-6_25"},{"key":"5_CR21","doi-asserted-by":"publisher","unstructured":"Fiter\u0103u-Bro\u015ftean, P., Lenaerts, T., Poll, E., de\u00a0Ruiter, J., Vaandrager, F., Verleg, P.: Model learning and model checking of SSH implementations. In: Proceedings of the 24th ACM SIGSOFT International SPIN Symposium on Model Checking of Software. pp. 142\u2013151. ACM, New York, NY, USA (Jul 2017). https:\/\/doi.org\/10.1145\/3092282.3092289","DOI":"10.1145\/3092282.3092289"},{"key":"5_CR22","doi-asserted-by":"publisher","unstructured":"Frohme, M., Steffen, B.: Compositional learning of mutually recursive procedural systems. International Journal on Software Tools for Technology Transfer 23(4), 521\u2013543 (Aug 2021). https:\/\/doi.org\/10.1007\/s10009-021-00634-y","DOI":"10.1007\/s10009-021-00634-y"},{"key":"5_CR23","doi-asserted-by":"publisher","unstructured":"Frohme, M., Steffen, B.: Never-stop context-free learning. In: Olderog, E.R., Steffen, B., Yi, W. (eds.) Model Checking, Synthesis, and Learning, LNCS, vol. 13030, pp. 164\u2013185. Springer International Publishing, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-91384-7_9","DOI":"10.1007\/978-3-030-91384-7_9"},{"key":"5_CR24","doi-asserted-by":"publisher","unstructured":"Garg, P., L\u00f6ding, C., Madhusudan, P., Neider, D.: Learning universally quantified invariants of linear data structures. In: Sharygina, N., Veith, H. (eds.) Computer Aided Verification. LNCS, vol.\u00a08044, pp. 813\u2013829. Springer, Berlin, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-39799-8_57","DOI":"10.1007\/978-3-642-39799-8_57"},{"key":"5_CR25","doi-asserted-by":"publisher","unstructured":"Groz, R., Irfan, M.N., Oriat, C.: Algorithmic improvements on regular inference of software models and perspectives for security testing. In: Proc. ISoLA 2012, Part I. LNCS, vol.\u00a07609, pp. 444\u2013457. Springer (2012). https:\/\/doi.org\/10.1007\/978-3-642-34026-0_41","DOI":"10.1007\/978-3-642-34026-0_41"},{"key":"5_CR26","doi-asserted-by":"publisher","unstructured":"Hagerer, A., Hungar, H., Niese, O., Steffen, B.: Model generation by moderated regular extrapolation. In: Kutsche, R.D., Weber, H. (eds.) Fundamental Approaches to Software Engineering, 5th International Conference, FASE 2002. LNCS, vol.\u00a02306, pp. 80\u201395. Springer Verlag (Apr 2002). https:\/\/doi.org\/10.1007\/3-540-45923-5_6","DOI":"10.1007\/3-540-45923-5_6"},{"key":"5_CR27","doi-asserted-by":"publisher","unstructured":"de\u00a0la Higuera, C.: A bibliographical study of grammatical inference. Pattern Recognition 38(9), 1332\u20131348 (Sep 2005). https:\/\/doi.org\/10.1016\/j.patcog.2005.01.003","DOI":"10.1016\/j.patcog.2005.01.003"},{"key":"5_CR28","doi-asserted-by":"publisher","unstructured":"Howar, F., Steffen, B.: Active automata learning in practice. In: Bennaceur, A., H\u00e4hnle, R., Meinke, K. (eds.) Machine Learning for Dynamic Software Analysis: Potentials and Limits, LNCS, vol. 11026, pp. 123\u2013148. Springer International Publishing, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-96562-8_5","DOI":"10.1007\/978-3-319-96562-8_5"},{"key":"5_CR29","doi-asserted-by":"publisher","unstructured":"Howar, F., Steffen, B.: Active automata learning as black-box search and lazy partition refinement. In: Jansen, N., Stoelinga, M., van\u00a0den Bos, P. (eds.) A Journey from Process Algebra via Timed Automata to Model Learning - Essays Dedicated to Frits Vaandrager on the Occasion of His 60th Birthday. LNCS, vol. 13560, pp. 321\u2013338. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-15629-8_17","DOI":"10.1007\/978-3-031-15629-8_17"},{"key":"5_CR30","doi-asserted-by":"publisher","unstructured":"Howar, F., Steffen, B., Jonsson, B., Cassel, S.: Inferring canonical register automata. In: Kuncak, V., Rybalchenko, A. (eds.) Verification, Model Checking, and Abstract Interpretation. LNCS, vol.\u00a07148, pp. 251\u2013266. Springer, Berlin, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-27940-9_17","DOI":"10.1007\/978-3-642-27940-9_17"},{"key":"5_CR31","doi-asserted-by":"publisher","unstructured":"Hungar, H., Niese, O., Steffen, B.: Domain-specific optimization in automata learning. In: Computer Aided Verification, 15th International Conference. LNCS, vol.\u00a02725, pp. 315\u2013327 (Jul 2003). https:\/\/doi.org\/10.1007\/978-3-540-45069-6_31","DOI":"10.1007\/978-3-540-45069-6_31"},{"key":"5_CR32","doi-asserted-by":"publisher","unstructured":"Isberner, M., Howar, F., Steffen, B.: The TTT algorithm: A redundancy-free approach to active automata learning. In: Runtime Verification: 5th International Conference, RV 2014, Proceedings. LNCS, vol.\u00a08734, pp. 307\u2013322. Springer (Sep 2014). https:\/\/doi.org\/10.1007\/978-3-319-11164-3_26","DOI":"10.1007\/978-3-319-11164-3_26"},{"key":"5_CR33","doi-asserted-by":"crossref","unstructured":"Kearns, M., Vazirani, U.: An Introduction to Computational Learning Theory. MIT Press (1994)","DOI":"10.7551\/mitpress\/3897.001.0001"},{"key":"5_CR34","doi-asserted-by":"publisher","unstructured":"Linard, A., de\u00a0la Higuera, C., Vaandrager, F.: Learning unions of $$k$$-testable languages. In: Mart\u00edn-Vide, C., Okhotin, A., Shapira, D. (eds.) Language and Automata Theory and Applications. LNCS, vol. 11417, pp. 328\u2013339. Springer International Publishing, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-13435-8_24","DOI":"10.1007\/978-3-030-13435-8_24"},{"key":"5_CR35","doi-asserted-by":"publisher","unstructured":"Maler, O., Mens, I.E.: Learning regular languages over large alphabets. In: Tools and Algorithms for the Construction and Analysis of Systems - 20th International Conference,. LNCS, vol.\u00a08413, pp. 485\u2013499. Springer (2014). https:\/\/doi.org\/10.1007\/978-3-642-54862-8_41","DOI":"10.1007\/978-3-642-54862-8_41"},{"key":"5_CR36","doi-asserted-by":"publisher","unstructured":"Margaria, T., Niese, O., Raffelt, H., Steffen, B.: Efficient test-based model generation for legacy reactive systems. In: Proceedings of the Ninth IEEE International High-Level Design Validation and Test Workshop. pp. 95\u2013100. IEEE, New York, NY, USA (Nov 2004). https:\/\/doi.org\/10.1109\/HLDVT.2004.1431246","DOI":"10.1109\/HLDVT.2004.1431246"},{"key":"5_CR37","doi-asserted-by":"publisher","unstructured":"Merten, M., Howar, F., Steffen, B., Cassel, S., Jonsson, B.: Demonstrating learning of register automata. In: Flanagan, C., K\u00f6nig, B. (eds.) Tools and Algorithms for the Construction and Analysis of Systems. LNCS, vol.\u00a07214, pp. 466\u2013471. Springer, Berlin, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-28756-5_32","DOI":"10.1007\/978-3-642-28756-5_32"},{"key":"5_CR38","doi-asserted-by":"publisher","unstructured":"Moerman, J., Sammartino, M., Silva, A., Klin, B., Szynwelski, M.: Learning nominal automata. In: Proc. 44$$^{th}$$ ACM Symp. on Principles of Programming Languages. pp. 613\u2013625. POPL \u201917, ACM, New York, NY, USA (Jan 2017). https:\/\/doi.org\/10.1145\/3093333.3009879","DOI":"10.1145\/3093333.3009879"},{"key":"5_CR39","doi-asserted-by":"publisher","unstructured":"Neider, D., Smetsers, R., Vaandrager, F.W., Kuppens, H.: Benchmarks for automata learning and conformance testing. In: Models, Mindsets, Meta: The What, the How, and the Why Not? - Essays Dedicated to Bernhard Steffen on the Occasion of His 60th Birthday. LNCS, vol. 11200, pp. 390\u2013416. Springer (2018). https:\/\/doi.org\/10.1007\/978-3-030-22348-9_23","DOI":"10.1007\/978-3-030-22348-9_23"},{"key":"5_CR40","doi-asserted-by":"publisher","unstructured":"Rivest, R.L., Schapire, R.E.: Inference of finite automata using homing sequences. Information and Computation 103(2), 299\u2013347 (1993). https:\/\/doi.org\/10.1006\/inco.1993.1021","DOI":"10.1006\/inco.1993.1021"},{"key":"5_CR41","unstructured":"de\u00a0Ruiter, J., Poll, E.: Protocol state fuzzing of TLS implementations. In: 24th USENIX Security Symposium (USENIX Security 15). pp. 193\u2013206. USENIX Association (Aug 2015), https:\/\/www.usenix.org\/conference\/usenixsecurity15\/technical-sessions\/presentation\/de-ruiter"},{"key":"5_CR42","doi-asserted-by":"publisher","unstructured":"Sagonas, K., Jonsson, B., Howar, F., Dierl, S., Fiterau-Brostean, P., T\u00e5quist, F.: Reproduction artifact for TACAS 2024 paper \u201cScalable tree-based register automata learning\u201d (Dec 2023). https:\/\/doi.org\/10.5281\/zenodo.10442556","DOI":"10.5281\/zenodo.10442556"},{"key":"5_CR43","doi-asserted-by":"publisher","unstructured":"Schuts, M., Hooman, J., Vaandrager, F.: Refactoring of legacy software using model learning and equivalence checking: An industrial experience report. In: \u00c1brah\u00e1m, E., Huisman, M. (eds.) Integrated Formal Methods. LNCS, vol.\u00a09681, pp. 311\u2013325. Springer International Publishing, Cham (2016).https:\/\/doi.org\/10.1007\/978-3-319-33693-0_20","DOI":"10.1007\/978-3-319-33693-0_20"},{"key":"5_CR44","doi-asserted-by":"publisher","unstructured":"Shahbaz, M., Groz, R.: Analysis and testing of black-box component-based systems by inferring partial models. Software Testing, Verification and Reliability 24(4), 253\u2013288 (2014). https:\/\/doi.org\/10.1002\/stvr.1491","DOI":"10.1002\/stvr.1491"},{"key":"5_CR45","doi-asserted-by":"publisher","unstructured":"Shu, G., Lee, D.: Testing security properties of protocol implementations - a machine learning based approach. In: 27th IEEE International Conference on Distributed Computing Systems (ICDCS 2007). IEEE Computer Society (2007). https:\/\/doi.org\/10.1109\/ICDCS.2007.147","DOI":"10.1109\/ICDCS.2007.147"},{"key":"5_CR46","doi-asserted-by":"publisher","unstructured":"Sun, J., Xiao, H., Liu, Y., Lin, S.W., Qin, S.: TLV: abstraction through testing, learning, and validation. In: Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering. pp. 698\u2013709. ACM, New York, NY, USA (Aug 2015). https:\/\/doi.org\/10.1145\/2786805.2786817","DOI":"10.1145\/2786805.2786817"},{"key":"5_CR47","doi-asserted-by":"publisher","unstructured":"Tappler, M., Aichernig, B.K., Bloem, R.: Model-based testing IoT communication via active automata learning. In: IEEE International Conference on Software Testing, Verification and Validation. pp. 276\u2013287. IEEE Computer Society (Mar 2017). https:\/\/doi.org\/10.1109\/ICST.2017.32","DOI":"10.1109\/ICST.2017.32"},{"key":"5_CR48","doi-asserted-by":"publisher","unstructured":"Vaandrager, F., Bloem, R., Ebrahimi, M.: Learning Mealy machines with one timer. In: Leporati, A., Mart\u00edn-Vide, C., Shapira, D., Zandron, C. (eds.) Language and Automata Theory and Applications. LNCS, vol. 12638, pp. 157\u2013170. Springer International Publishing, Cham (2021).https:\/\/doi.org\/10.1007\/978-3-030-68195-1_13","DOI":"10.1007\/978-3-030-68195-1_13"},{"key":"5_CR49","doi-asserted-by":"publisher","unstructured":"Vaandrager, F., Garhewal, B., Rot, J., Wi\u00dfmann, T.: A new approach for active automata learning based on apartness. In: Fisman, D., Rosu, G. (eds.) Tools and Algorithms for the Construction and Analysis of Systems. LNCS, vol. 13243, pp. 223\u2013243. Springer International Publishing, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-030-99524-9_12","DOI":"10.1007\/978-3-030-99524-9_12"},{"key":"5_CR50","doi-asserted-by":"publisher","unstructured":"Vaandrager, F.W.: Model learning. Commun. ACM 60(2), 86\u201395 (Jan 2017). https:\/\/doi.org\/10.1145\/2967606","DOI":"10.1145\/2967606"},{"key":"5_CR51","doi-asserted-by":"publisher","unstructured":"Volpato, M., Tretmans, J.: Active learning of nondeterministic systems from an ioco perspective. In: Margaria, T., Steffen, B. (eds.) Leveraging Applications of Formal Methods, Verification and Validation. Technologies for Mastering Change. LNCS, vol.\u00a08802, pp. 220\u2013235. Springer, Berlin, Heidelberg (2014). https:\/\/doi.org\/10.1007\/978-3-662-45234-9_16","DOI":"10.1007\/978-3-662-45234-9_16"},{"key":"5_CR52","doi-asserted-by":"publisher","unstructured":"Walkinshaw, N., Bogdanov, K., Derrick, J., Par\u00eds, J.: Increasing functional coverage by inductive testing: A case study. In: Testing Software and Systems - 22nd IFIP WG 6.1 International Conference, ICTSS 2010. LNCS, vol.\u00a06435, pp. 126\u2013141. Springer (2010). https:\/\/doi.org\/10.1007\/978-3-642-16573-3_10","DOI":"10.1007\/978-3-642-16573-3_10"},{"key":"5_CR53","doi-asserted-by":"publisher","unstructured":"Yonesaki, N., Katayama, T.: Functional specification of synchronized processes based on modal logic. In: Proceedings of the 6th Int. Conference on Software Engineering. pp. 208\u2013217. IEEE Computer Society Press (1982). https:\/\/doi.org\/10.5555\/800254.807763","DOI":"10.5555\/800254.807763"}],"container-title":["Lecture Notes in Computer Science","Tools and Algorithms for the Construction and Analysis of Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-57249-4_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,4]],"date-time":"2024-04-04T07:06:21Z","timestamp":1712214381000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-57249-4_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031572487","9783031572494"],"references-count":53,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-57249-4_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"5 April 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"TACAS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Tools and Algorithms for the Construction and Analysis of Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Luxembourg City","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Luxembourg","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 April 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 April 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"tacas2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/etaps.org\/2024\/conferences\/tacas\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"159","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"53","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"16","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"33% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"10","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}