C. Richter, J. F. Haltermann, M.-C. Jakobs, F. Pauck, S. Schott, and H. Wehrheim, “Are Neural Bug Detectors Comparable to Software Developers on Variable Misuse Bugs?,”10.1145/3551349.3561156__2023, doi:10.1145/3551349.3561156 .

F. Pauck, “Scaling Arbitrary Android App Analyses,”10.1145/3551349.3561339__2023, doi:10.1145/3551349.3561339 .

Q. A. Ahmed, M. Awais, and M. Platzner, “MAAS: Hiding Trojans in Approximate Circuits,” presented at the The 24th International Symposium on Quality Electronic Design (ISQED’23), San Fransico CA 94023-0607, USA, 2023.

F. Pauck, Cooperative Android App Analysis. Paderborn University, 2023.

J. Kersting, Identifizierung quantifizierbarer Bewertungsinhalte und -kategorien mittels Text Mining. Neubiberg: Universität der Bundeswehr München , 2023.

H. Wehrheim, M. Platzner, E. Bodden, P. Schubert, F. Pauck, and M.-C. Jakobs, “Verifying Software and Reconfigurable Hardware Services,” in On-The-Fly Computing -- Individualized IT-services in dynamic markets, vol. 412, C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, and H. Wehrheim, Eds. Paderborn: Heinz Nixdorf Institut, Universität Paderborn, 2023, pp. 125–144.

F. S. Bäumer, W.-F. Chen, M. Geierhos, J. Kersting, and H. Wachsmuth, “Dialogue-based Requirement Compensation and Style-adjusted Data-to-text Generation,” in On-The-Fly Computing -- Individualized IT-services in dynamic markets, vol. 412, C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, and H. Wehrheim, Eds. Paderborn: Heinz Nixdorf Institut, Universität Paderborn, 2023, pp. 65–84.

J. M. Hanselle et al., “Configuration and Evaluation,” in On-The-Fly Computing -- Individualized IT-services in dynamic markets, vol. 412, C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, and H. Wehrheim, Eds. Paderborn: Heinz Nixdorf Institut, Universität Paderborn, 2023, pp. 85–104.

H. Wehrheim, E. Hüllermeier, S. Becker, M. Becker, C. Richter, and A. Sharma, “Composition Analysis in Unknown Contexts,” in On-The-Fly Computing -- Individualized IT-services in dynamic markets, vol. 412, C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, and H. Wehrheim, Eds. Paderborn: Heinz Nixdorf Institut, Universität Paderborn, 2023, pp. 105–123.

J. Kersting and M. Geierhos, “Towards Comparable Ratings: Quantifying Evaluative Phrases in Physician Reviews,” in Data Management Technologies and Applications, vol. 1860, A. Cuzzocrea, O. Gusikhin, S. Hammoudi, and C. Quix, Eds. Cham: Springer Nature Switzerland, 2023, pp. 45–65.

A. Tornede, Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions. 2023.

C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, and H. Wehrheim, On-The-Fly Computing -- Individualized IT-services in dynamic markets, vol. 412. Paderborn: Heinz Nixdorf Institut, Universität Paderborn, 2023.

J. König, On the Membership and Correctness Problem for State Serializability and Value Opacity. 2023.

L. M. Witschen, T. Wiersema, L. D. Reuter, and M. Platzner, “Search Space Characterization for Approximate Logic Synthesis ,” presented at the 2022 59th ACM/IEEE Design Automation Conference (DAC), San Francisco, USA.

L. M. Witschen, T. Wiersema, M. Artmann, and M. Platzner, “MUSCAT: MUS-based Circuit Approximation Technique,” presented at the Design, Automation and Test in Europe (DATE), Online.

E. Schede et al., “A Survey of Methods for Automated Algorithm Configuration,” arXiv:2202.01651. 2022.

A. Sharma, V. Melnikov, E. Hüllermeier, and H. Wehrheim, “Property-Driven Testing of Black-Box Functions,” in Proceedings of the 10th IEEE/ACM International Conference on Formal Methods in Software Engineering (FormaliSE), 2022, pp. 113–123.

L. Fehring, J. M. Hanselle, and A. Tornede, “HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection,” presented at the Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022, Baltimore, 2022.

P. Schubert et al., “Static data-flow analysis for software product lines in C,”10.1007/s10515-022-00333-1__Automated Software Engineering, vol. 29, no. 1, Art. no. 35, 2022, doi:10.1007/s10515-022-00333-1 .

C. Richter and H. Wehrheim, “Learning Realistic Mutations: Bug Creation for Neural Bug Detectors,”10.1109/ICST53961.2022.00027__in 2022 IEEE Conference on Software Testing, Verification and Validation (ICST), 2022, pp. 162–173, doi:10.1109/ICST53961.2022.00027 .

C. Richter and H. Wehrheim, “TSSB-3M: Mining single statement bugs at massive scale,”10.1145/3524842.3528505__in 2022 IEEE/ACM 19th International Conference on Mining Software Repositories (MSR), 2022, pp. 418–422, doi:10.1145/3524842.3528505 .

K. Dreßler, M. Sherif, and A.-C. Ngonga Ngomo, “ADAGIO - Automated Data Augmentation of Knowledge Graphs Using Multi-expression Learning,”10.1145/3511095.3531287__presented at the HT ’22: 33rd ACM Conference on Hypertext and Social Media, Barcelona (Spain), 2022, doi:10.1145/3511095.3531287 .

W.-F. Chen, M.-H. Chen, G. Mudgal, and H. Wachsmuth, “Analyzing Culture-Specific Argument Structures in Learner Essays,” in Proceedings of the 9th Workshop on Argument Mining (ArgMining 2022), 2022, pp. 51–61.

J. Kersting, M. Ahmed, and M. Geierhos, “Chatbot-Enhanced Requirements Resolution for Automated Service Compositions,” in HCI International 2022 Posters, vol. 1580, C. Stephanidis, M. Antona, and S. Ntoa, Eds. Cham, Switzerland: Springer International Publishing, 2022, pp. 419--426.

J. Kersting and F. S. Bäumer, “Implicit Statements in Healthcare Reviews: A Challenge for Sentiment Analysis,” in Proceedings of the Fourteenth International Conference on Pervasive Patterns and Applications (PATTERNS 2022): Special Track AI-DRSWA: Maturing Artificial Intelligence - Data Science for Real-World Applications, Barcelona, Spain, 2022, pp. 5–9.

M.-H. Chen, G. Mudgal, W.-F. Chen, and H. Wachsmuth, “Investigating the argumentation structures of EFL learners from diverse language backgrounds,” 2022.

L. Fehring, Combined Ranking and Regression Trees for Algorithm Selection. Paderborn, 2022.

A. Tornede, V. Bengs, and E. Hüllermeier, “Machine Learning for Online Algorithm Selection under Censored Feedback,” Proceedings of the 36th AAAI Conference on Artificial Intelligence. AAAI, 2022.

A. Tornede, L. Gehring, T. Tornede, M. D. Wever, and E. Hüllermeier, “Algorithm Selection on a Meta Level,” Machine Learning. 2022.

K. Gevers, A. Tornede, M. D. Wever, V. Schöppner, and E. Hüllermeier, “A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials,”10.1007/s40194-022-01339-9__Welding in the World, 2022, doi:10.1007/s40194-022-01339-9 .

L. M. Witschen, Frameworks and Methodologies for Search-based Approximate Logic Synthesis. 2022.

Q. A. Ahmed and M. Platzner, “On the Detection and Circumvention of Bitstream-Level Trojans in FPGAs,” presented at the IEEE Computer Society Annual Symposium on VLSI Aliathon Resort, Pafos, Cyprus, 2022.

M. Ahmed, Knowledge Base Enhanced &; User-centric Dialogue Design for OTF Computing. 2022.

J. Palushi, Domain-aware Text Professionalization using Sequence-to-Sequence Neural Networks. 2022.

V. Budanurmath, Propaganda Technique Detection Using Connotation Frames. 2022.

B. Dongol, G. Schellhorn, and H. Wehrheim, “Weak Progressive Forward Simulation Is Necessary and Sufficient for Strong Observational Refinement,”10.4230/LIPIcs.CONCUR.2022.31__in 33rd International Conference on Concurrency Theory, CONCUR 2022, September 12-16, 2022, Warsaw, Poland, 2022, vol. 243, p. 31:1–31:23, doi:10.4230/LIPIcs.CONCUR.2022.31 .

A. Sharma, C. Demir, A.-C. Ngonga Ngomo, and H. Wehrheim, “MLCHECK–Property-Driven Testing of Machine Learning Classifiers.”

F. S. Bäumer, J. Kersting, S. Denisov, and M. Geierhos, “IN OTHER WORDS: A NAIVE APPROACH TO TEXT SPINNING,” in PROCEEDINGS OF THE INTERNATIONAL CONFERENCES ON WWW/INTERNET 2021 AND APPLIED COMPUTING 2021, Lisbon, Portugal, 2021, pp. 221--225.

T. Wiersema, Guaranteeing Properties of Reconfigurable Hardware Circuits with Proof-Carrying Hardware. Paderborn: Paderborn University, 2021.

L. Luo et al., “TaintBench: Automatic real-world malware benchmarking of Android taint analyses,”10.1007/s10664-021-10013-5__Empirical Software Engineering, 2021, doi:10.1007/s10664-021-10013-5 .

M. D. Wever, A. Tornede, F. Mohr, and E. Hüllermeier, “AutoML for Multi-Label Classification: Overview and Empirical Evaluation,”10.1109/tpami.2021.3051276__IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1–1, 2021, doi:10.1109/tpami.2021.3051276 .

F. Mohr, M. D. Wever, A. Tornede, and E. Hüllermeier, “Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning,” IEEE Transactions on Pattern Analysis and Machine Intelligence.

T. Tornede, A. Tornede, M. D. Wever, and E. Hüllermeier, “Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance,” presented at the Genetic and Evolutionary Computation Conference, 2021.

J. Kersting and M. Geierhos, “Towards Aspect Extraction and Classification for Opinion Mining with Deep Sequence Networks,” in Natural Language Processing in Artificial Intelligence -- NLPinAI 2020, vol. 939, R. Loukanova, Ed. Cham: Springer, 2021, pp. 163--189.

S. Schott, Android App Analysis Benchmark Case Generation. Paderborn, 2021.

E. Hüllermeier, F. Mohr, A. Tornede, and M. D. Wever, “Automated Machine Learning, Bounded Rationality, and Rational Metareasoning,” presented at the ECML/PKDD Workshop on Automating Data Science, Bilbao (Virtual), 2021.

J. Derrick, S. Doherty, B. Dongol, G. Schellhorn, and H. Wehrheim, “On Strong Observational Refinement and Forward Simulation,” in Proceedings of the 35th International Symposium on Distributed Computing (DISC).

J. Kersting and M. Geierhos, “Well-being in Plastic Surgery: Deep Learning Reveals Patients’ Evaluations,” in Proceedings of the 10th International Conference on Data Science, Technology and Applications (DATA 2021), Online, 2021, pp. 275--284.

L. M. Witschen, T. Wiersema, M. Raeisi Nafchi, A. Bockhorn, and M. Platzner, “Timing Optimization for Virtual FPGA Configurations,”10.1007/978-3-030-79025-7_4__in Proceedings of International Symposium on Applied Reconfigurable Computing (ARC’21), Virtual conference, doi:10.1007/978-3-030-79025-7_4 .

P. Schubert, B. Hermann, and E. Bodden, “Lossless, Persisted Summarization of Static Callgraph, Points-To and Data-Flow Analysis,” 2021.