Benchmarking Combinations of Learning and Testing Algorithms for Active Automata Learning

Active automata learning comprises techniques for learning automata models of black-box systems by testing such systems. While this form of learning enables model-based analysis and verification, it may also require a substantial amount of interactions with considered systems to learn adequate models, which capture the systems’ behaviour.

[1]  Bernhard Steffen,et al.  Introduction to Active Automata Learning from a Practical Perspective , 2011, SFM.

[2]  Bernhard Steffen,et al.  From ZULU to RERS - Lessons Learned in the ZULU Challenge , 2010, ISoLA.

[3]  Bernhard K. Aichernig,et al.  Efficient Active Automata Learning via Mutation Testing , 2018, Journal of Automated Reasoning.

[4]  M. P. Vasilevskii.  Failure diagnosis of automata , 1973 .

[5]  Bernhard K. Aichernig,et al.  Model-Based Testing IoT Communication via Active Automata Learning , 2017, 2017 IEEE International Conference on Software Testing, Verification and Validation (ICST).

[6]  Frits W. Vaandrager,et al.  Applying Automata Learning to Embedded Control Software , 2015, ICFEM.

[7]  Frits W. Vaandrager,et al.  Model learning and model checking of SSH implementations , 2017, SPIN.

[8]  Sicco Verwer,et al.  Complementing Model Learning with Mutation-Based Fuzzing , 2016, ArXiv.

[9]  Colin de la Higuera,et al.  Zulu: An Interactive Learning Competition , 2009, FSMNLP.

[10]  Bernhard K. Aichernig,et al.  Automata Learning for Symbolic Execution , 2018, 2018 Formal Methods in Computer Aided Design (FMCAD).

[11]  Frits W. Vaandrager,et al.  Model learning , 2017, Commun. ACM.

[12]  Roland Groz,et al.  Inferring Mealy Machines , 2009, FM.

[13]  Bernhard Steffen,et al.  The TTT Algorithm: A Redundancy-Free Approach to Active Automata Learning , 2014, RV.

[14]  Joeri de Ruiter,et al.  Protocol State Fuzzing of TLS Implementations , 2015, USENIX Security Symposium.

[15]  Tsun S. Chow,et al.  Testing Software Design Modeled by Finite-State Machines , 1978, IEEE Transactions on Software Engineering.

[16]  Ferhat Khendek,et al.  Test Selection Based on Finite State Models , 1991, IEEE Trans. Software Eng..

[17]  Roland Groz,et al.  Using Adaptive Sequences for Learning Non-Resettable FSMs , 2018, ICGI.

[18]  Bernhard Steffen,et al.  The Open-Source LearnLib - A Framework for Active Automata Learning , 2015, CAV.

[19]  Bernhard K. Aichernig,et al.  Learning a Behavior Model of Hybrid Systems Through Combining Model-Based Testing and Machine Learning (Full Version) , 2019, ICTSS.

[20]  Frits W. Vaandrager,et al.  Combining Model Learning and Model Checking to Analyze TCP Implementations , 2016, CAV.

[21]  Bengt Jonsson,et al.  Insights to Angluin's Learning , 2005, SVV@ICLP.

[22]  Karl Meinke,et al.  Learning-based testing for autonomous systems using spatial and temporal requirements , 2018, MASES@ASE.

[23]  Tiziana Margaria,et al.  Efficient test-based model generation for legacy reactive systems , 2004, Proceedings. Ninth IEEE International High-Level Design Validation and Test Workshop (IEEE Cat. No.04EX940).

[24]  Bernhard K. Aichernig,et al.  Model Learning and Model-Based Testing , 2018, Machine Learning for Dynamic Software Analysis.

[25]  Roland Groz,et al.  Case Studies in Learning Models and Testing Without Reset , 2019, 2019 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW).

[26]  Oliver Niese,et al.  An integrated approach to testing complex systems , 2003 .

[27]  Ronald L. Rivest,et al.  Inference of finite automata using homing sequences , 1989, STOC '89.

[28]  Frits W. Vaandrager,et al.  Benchmarks for Automata Learning and Conformance Testing , 2018, Models, Mindsets, Meta.

[29]  Dana Angluin,et al.  Learning Regular Sets from Queries and Counterexamples , 1987, Inf. Comput..