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.

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.

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

J. Kersting, F. Maoro, and M. Geierhos, “Towards comparable ratings: Exploring bias in German physician reviews,”10.1016/j.datak.2023.102235__Data &; Knowledge Engineering, vol. 148, Art. no. 102235, 2023, doi:10.1016/j.datak.2023.102235 .

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.

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.

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 .