Symbolic Input-Output Conformance Checking for Model-Based Mutation Testing. - Bernhard K. Aichernig; Martin Tappler (USE@FM, 2015)

Conformance Checking of Real-Time Models - Symbolic Execution vs. Bounded Model Checking. - Bernhard K. Aichernig; Florian Lorber; Martin Tappler (Theory and Practice of Formal Methods, 2016)

Does this fault lead to failure? Combining refinement and input-output conformance checking in fault-oriented test-case generation. - Bernhard K. Aichernig; Elisabeth Jöbstl; Martin Tappler (J. Log. Algebraic Methods Program., 2016)

Model-Based Testing IoT Communication via Active Automata Learning. - Martin Tappler; Bernhard K. Aichernig; Roderick Bloem (ICST, 2017)

Learning from Faults: Mutation Testing in Active Automata Learning. - Bernhard K. Aichernig; Martin Tappler (NFM, 2017)

Probabilistic Black-Box Reachability Checking. - Bernhard K. Aichernig; Martin Tappler (RV, 2017)

Dependable Internet of Things for Networked Cars. - Bernhard Großwindhager; Astrid Rupp; Martin Tappler; Markus Tranninger; Samuel Weiser; Bernhard K. Aichernig; Carlo Alberto Boano; Martin Horn; Gernot Kubin; Stefan Mangard; Martin Steinberger; Kay Römer (Int. J. Comput., 2017)

Model Learning and Model-Based Testing. - Bernhard K. Aichernig; Wojciech Mostowski; Mohammad Reza Mousavi 001; Martin Tappler; Masoumeh Taromirad (Machine Learning for Dynamic Software Analysis, 2018)

Automata Learning for Symbolic Execution. - Bernhard K. Aichernig; Roderick Bloem; Masoud Ebrahimi 002; Martin Tappler; Johannes Winter (FMCAD, 2018)

Learning Timed Automata via Genetic Programming. - Martin Tappler; Bernhard K. Aichernig; Kim Guldstrand Larsen; Florian Lorber (CoRR, 2018)

L*-Based Learning of Markov Decision Processes. - Martin Tappler; Bernhard K. Aichernig; Giovanni Bacci 001; Maria Eichlseder; Kim G. Larsen (FM, 2019)

Time to Learn - Learning Timed Automata from Tests. - Martin Tappler; Bernhard K. Aichernig; Kim Guldstrand Larsen; Florian Lorber (FORMATS, 2019)

Learning a Behavior Model of Hybrid Systems Through Combining Model-Based Testing and Machine Learning. - Bernhard K. Aichernig; Roderick Bloem; Masoud Ebrahimi 002; Martin Horn; Franz Pernkopf; Wolfgang Roth; Astrid Rupp; Martin Tappler; Markus Tranninger (ICTSS, 2019)

L*-Based Learning of Markov Decision Processes (Extended Version). - Martin Tappler; Bernhard K. Aichernig; Giovanni Bacci 001; Maria Eichlseder; Kim G. Larsen (CoRR, 2019)

Learning a Behavior Model of Hybrid Systems Through Combining Model-Based Testing and Machine Learning (Full Version). - Bernhard K. Aichernig; Roderick Bloem; Masoud Ebrahimi 002; Martin Horn; Franz Pernkopf; Wolfgang Roth; Astrid Rupp; Martin Tappler; Markus Tranninger (CoRR, 2019)

Probabilistic black-box reachability checking (extended version). - Bernhard K. Aichernig; Martin Tappler (Formal Methods Syst. Des., 2019)

Efficient Active Automata Learning via Mutation Testing. - Bernhard K. Aichernig; Martin Tappler (J. Autom. Reason., 2019)

From Passive to Active: Learning Timed Automata Efficiently. - Bernhard K. Aichernig; Andrea Pferscher; Martin Tappler (NFM, 2020)

Benchmarking Combinations of Learning and Testing Algorithms for Active Automata Learning. - Bernhard K. Aichernig; Martin Tappler; Felix Wallner (TAP@STAF, 2020)

Adaptive Shielding under Uncertainty. - Stefan Pranger; Bettina Könighofer; Martin Tappler; Martin Deixelberger; Nils Jansen 001; Roderick Bloem (ACC, 2021)

Online Shielding for Stochastic Systems. - Bettina Könighofer; Julian Rudolf; Alexander Palmisano; Martin Tappler; Roderick Bloem (NFM, 2021)

Active Model Learning of Stochastic Reactive Systems. - Martin Tappler; Edi Muskardin; Bernhard K. Aichernig; Ingo Pill (SEFM, 2021)

Health of smart ecosystems. - Noura El Moussa; Davide Molinelli; Mauro Pezzè; Martin Tappler (ESEC/SIGSOFT FSE, 2021)

L*-based learning of Markov decision processes (extended version). - Martin Tappler; Bernhard K. Aichernig; Giovanni Bacci 001; Maria Eichlseder; Kim G. Larsen (Formal Aspects Comput., 2021)

Testing Software in Production Environments with Data from the Field. - Luca Gazzola; Leonardo Mariani; Matteo Orrù; Mauro Pezzè; Martin Tappler (ICST, 2022)

Learning Finite State Models fromRecurrent Neural Networks. - Edi Muskardin; Bernhard K. Aichernig; Ingo Pill; Martin Tappler (IFM, 2022)

Search-Based Testing of Reinforcement Learning. - Martin Tappler; Filip Cano Córdoba; Bernhard K. Aichernig; Bettina Könighofer (IJCAI, 2022)

Automata Learning Meets Shielding. - Martin Tappler; Stefan Pranger; Bettina Könighofer; Edi Muskardin; Roderick Bloem; Kim G. Larsen (ISoLA (1), 2022)

Timed Automata Learning via SMT Solving. - Martin Tappler; Bernhard K. Aichernig; Florian Lorber (NFM, 2022)

Constrained Training of Recurrent Neural Networks for Automata Learning. - Bernhard K. Aichernig; Sandra König; Cristinel Mateis; Andrea Pferscher; Dominik Schmidt; Martin Tappler (SEFM, 2022)

AALpy: an active automata learning library. - Edi Muskardin; Bernhard K. Aichernig; Ingo Pill; Andrea Pferscher; Martin Tappler (Innov. Syst. Softw. Eng., 2022)

Testing-based Black-box Extraction of Simple Models from RNNs and Transformers. - Edi Muskardin; Martin Tappler; Bernhard K. Aichernig (ICGI, 2023)

Reinforcement Learning Under Partial Observability Guided by Learned Environment Models. - Edi Muskardin; Martin Tappler; Bernhard K. Aichernig; Ingo Pill (iFM, 2023)

Differential Safety Testing of Deep RL Agents Enabled by Automata Learning. - Martin Tappler; Bernhard K. Aichernig (AISoLA, 2023)

On the Relationship Between RNN Hidden State Vectors and Semantic Ground Truth. - Edi Muskardin; Martin Tappler; Ingo Pill; Bernhard K. Aichernig; Thomas Pock (CoRR, 2023)

Learning Environment Models with Continuous Stochastic Dynamics. - Martin Tappler; Edi Muskardin; Bernhard K. Aichernig; Bettina Könighofer (CoRR, 2023)

Online shielding for reinforcement learning. - Bettina Könighofer; Julian Rudolf; Alexander Palmisano; Martin Tappler; Roderick Bloem (Innov. Syst. Softw. Eng., 2023)

On the Relationship Between RNN Hidden-State Vectors and Semantic Structures. - Edi Muskardin; Martin Tappler; Ingo Pill; Bernhard K. Aichernig; Thomas Pock (ACL (Findings), 2024)

Learning and Repair of Deep Reinforcement Learning Policies from Fuzz-Testing Data. - Martin Tappler; Andrea Pferscher; Bernhard K. Aichernig; Bettina Könighofer (ICSE, 2024)

Active Model Learning of Git Version Control System. - Edi Muskardin; Tamim Burgstaller; Martin Tappler; Bernhard K. Aichernig (ICSTW, 2024)

Bridging the Gap Between Models in RL: Test Models vs. Neural Networks. - Martin Tappler; Florian Lorber (ICSTW, 2024)

Learning Environment Models with Continuous Stochastic Dynamics - with an Application to Deep RL Testing. - Martin Tappler; Edi Muskardin; Bernhard K. Aichernig; Bettina Könighofer (ICST, 2024)

Test Where Decisions Matter: Importance-driven Testing for Deep Reinforcement Learning. - Stefan Pranger; Hana Chockler; Martin Tappler; Bettina Könighofer (NeurIPS, 2024)

Hierarchical Learning of Generative Automaton Models from Sequential Data. - Benjamin von Berg; Bernhard K. Aichernig; Maximilian Rindler; Darko Stern; Martin Tappler (SEFM, 2024)

Benchmarking Combinations of Learning and Testing Algorithms for Automata Learning. - Bernhard K. Aichernig; Martin Tappler; Felix Wallner (Formal Aspects Comput., 2024)

Active model learning of stochastic reactive systems (extended version). - Edi Muskardin; Martin Tappler; Bernhard K. Aichernig; Ingo Pill (Softw. Syst. Model., 2024)

Learning minimal automata with recurrent neural networks. - Bernhard K. Aichernig; Sandra König; Cristinel Mateis; Andrea Pferscher; Martin Tappler (Softw. Syst. Model., 2024)

Mutating Skeletons: Learning Timed Automata via Domain Knowledge. - Felix Wallner; Bernhard K. Aichernig; Florian Lorber; Martin Tappler (ICSTW, 2025)

Rule-Guided Reinforcement Learning Policy Evaluation and Improvement. - Martin Tappler; Ignacio D. Lopez-Miguel; Sebastian Tschiatschek; Ezio Bartocci (CoRR, 2025)