Richter, Cedric, et al. “Are Neural Bug Detectors Comparable to Software Developers on Variable Misuse Bugs?”10.1145/3551349.3561156__37th IEEE/ACM International Conference on Automated Software Engineering, ACM, 2023, doi:10.1145/3551349.3561156.

Pauck, Felix. “Scaling Arbitrary Android App Analyses.”10.1145/3551349.3561339__37th IEEE/ACM International Conference on Automated Software Engineering, ACM, 2023, doi:10.1145/3551349.3561339.

Ahmed, Qazi Arbab, et al. “MAAS: Hiding Trojans in Approximate Circuits.” The 24th International Symposium on Quality Electronic Design (ISQED’23), San Francisco, Califorina USA, 2023.

Wehrheim, Heike, et al. “Verifying Software and Reconfigurable Hardware Services.”10.5281/zenodo.8068583__On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets, edited by Claus-Jochen Haake et al., vol. 412, Heinz Nixdorf Institut, Universität Paderborn, 2023, pp. 125–44, doi:10.5281/zenodo.8068583.

Bäumer, Frederik Simon, et al. “Dialogue-Based Requirement Compensation and Style-Adjusted Data-to-Text Generation.”10.5281/zenodo.8068456__On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets, edited by Claus-Jochen Haake et al., vol. 412, Heinz Nixdorf Institut, Universität Paderborn, 2023, pp. 65–84, doi:10.5281/zenodo.8068456.

Hanselle, Jonas Manuel, et al. “Configuration and Evaluation.”10.5281/zenodo.8068466__On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets, edited by Claus-Jochen Haake et al., vol. 412, Heinz Nixdorf Institut, Universität Paderborn, 2023, pp. 85–104, doi:10.5281/zenodo.8068466.

Wehrheim, Heike, et al. “Composition Analysis in Unknown Contexts.”10.5281/zenodo.8068510__On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets, edited by Claus-Jochen Haake et al., vol. 412, Heinz Nixdorf Institut, Universität Paderborn, 2023, pp. 105–23, doi:10.5281/zenodo.8068510.

Kersting, Joschka, and Michaela Geierhos. “Towards Comparable Ratings: Quantifying Evaluative Phrases in Physician Reviews.”10.1007/978-3-031-37890-4_3__Data Management Technologies and Applications, edited by Alfredo Cuzzocrea et al., vol. 1860, Springer Nature Switzerland, 2023, pp. 45–65, doi:10.1007/978-3-031-37890-4_3.

Haake, Claus-Jochen, et al. On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets. Heinz Nixdorf Institut, Universität Paderborn, 2023, doi:10.17619/UNIPB/1-1797.

Witschen, Linus Matthias, et al. “Search Space Characterization for Approximate Logic Synthesis .” 2022 59th ACM/IEEE Design Automation Conference (DAC).

Witschen, Linus Matthias, et al. “MUSCAT: MUS-Based Circuit Approximation Technique.” Design, Automation and Test in Europe (DATE).

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

Sharma, Arnab, et al. “Property-Driven Testing of Black-Box Functions.” Proceedings of the 10th IEEE/ACM International Conference on Formal Methods in Software Engineering (FormaliSE), IEEE, 2022, pp. 113–23.

Fehring, Lukass, et al. “HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection.” Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022, 2022.

Schubert, Philipp, 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, 35, Springer Science and Business Media LLC, 2022, doi:10.1007/s10515-022-00333-1.

Richter, Cedric, and Heike Wehrheim. “Learning Realistic Mutations: Bug Creation for Neural Bug Detectors.”10.1109/ICST53961.2022.00027__2022 IEEE Conference on Software Testing, Verification and Validation (ICST), 2022, pp. 162–73, doi:10.1109/ICST53961.2022.00027.

Richter, Cedric, and Heike Wehrheim. “TSSB-3M: Mining Single Statement Bugs at Massive Scale.”10.1145/3524842.3528505__2022 IEEE/ACM 19th International Conference on Mining Software Repositories (MSR), 2022, pp. 418–22, doi:10.1145/3524842.3528505.

Dreßler, Kevin, et al. “ADAGIO - Automated Data Augmentation of Knowledge Graphs Using Multi-Expression Learning.”10.1145/3511095.3531287__Proceedings of the 33rd ACM Conference on Hypertext and Hypermedia, 2022, doi:10.1145/3511095.3531287.

Chen, Wei-Fan, et al. “Analyzing Culture-Specific Argument Structures in Learner Essays.” Proceedings of the 9th Workshop on Argument Mining (ArgMining 2022), 2022, pp. 51–61.

Kersting, Joschka, et al. “Chatbot-Enhanced Requirements Resolution for Automated Service Compositions.”10.1007/978-3-031-06417-3_56__HCI International 2022 Posters, edited by Constantine Stephanidis et al., vol. 1580, Springer International Publishing, 2022, pp. 419--426, doi:10.1007/978-3-031-06417-3_56.

Kersting, Joschka, and Frederik Simon Bäumer. “Implicit Statements in Healthcare Reviews: A Challenge for Sentiment Analysis.” 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, edited by Joschka Kersting, IARIA, 2022, pp. 5–9.

Chen, Mei-Hua, et al. “Investigating the Argumentation Structures of EFL Learners from Diverse Language Backgrounds.” EUROCALL, 2022.

Tornede, Alexander, et al. “Machine Learning for Online Algorithm Selection under Censored Feedback.” Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI, 2022.

Tornede, Alexander, et al. “Algorithm Selection on a Meta Level.” Machine Learning, 2022.

Gevers, Karina, et al. “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, Springer Science and Business Media LLC, 2022, doi:10.1007/s40194-022-01339-9.

Ahmed, Qazi Arbab, and Marco Platzner. On the Detection and Circumvention of Bitstream-Level Trojans in FPGAs. IEEE Computer Society Annual Symposium on VLSI (ISVLSI,2022), 2022.

Dongol, Brijesh, et al. “Weak Progressive Forward Simulation Is Necessary and Sufficient for Strong Observational Refinement.”10.4230/LIPIcs.CONCUR.2022.31__33rd International Conference on Concurrency Theory, CONCUR 2022, September 12-16, 2022, Warsaw, Poland, edited by Bartek Klin et al., vol. 243, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022, p. 31:1–31:23, doi:10.4230/LIPIcs.CONCUR.2022.31.

Sharma, Arnab, et al. “MLCHECK–Property-Driven Testing of Machine Learning Classifiers.” Proceedings of the 20th IEEE International Conference on Machine Learning and Applications (ICMLA), IEEE.

Bäumer, Frederik Simon, et al. “IN OTHER WORDS: A NAIVE APPROACH TO TEXT SPINNING.” PROCEEDINGS OF THE INTERNATIONAL CONFERENCES ON WWW/INTERNET 2021 AND APPLIED COMPUTING 2021, IADIS, 2021, pp. 221--225.

Luo, Linghui, 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.

Wever, Marcel Dominik, et al. “AutoML for Multi-Label Classification: Overview and Empirical Evaluation.”10.1109/tpami.2021.3051276__IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, pp. 1–1, doi:10.1109/tpami.2021.3051276.

Mohr, Felix, et al. “Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning.” IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE.

Tornede, Tanja, et al. “Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance.” Proceedings of the Genetic and Evolutionary Computation Conference, 2021.

Kersting, Joschka, and Michaela Geierhos. “Towards Aspect Extraction and Classification for Opinion Mining with Deep Sequence Networks.”10.1007/978-3-030-63787-3_6__Natural Language Processing in Artificial Intelligence -- NLPinAI 2020, edited by Roussanka Loukanova, vol. 939, Springer, 2021, pp. 163--189, doi:10.1007/978-3-030-63787-3_6.

Hüllermeier, Eyke, et al. Automated Machine Learning, Bounded Rationality, and Rational Metareasoning. 2021.

Derrick, John, et al. “On Strong Observational Refinement and Forward Simulation.” Proceedings of the 35th International Symposium on Distributed Computing (DISC), Schloß Dagstuhl.

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

Witschen, Linus Matthias, et al. “Timing Optimization for Virtual FPGA Configurations.”10.1007/978-3-030-79025-7_4__Proceedings of International Symposium on Applied Reconfigurable Computing (ARC’21), edited by Frank Hannig et al., Springer Lecture Notes in Computer Science, doi:10.1007/978-3-030-79025-7_4.

Schubert, Philipp, et al. “Lossless, Persisted Summarization of Static Callgraph, Points-To and Data-Flow Analysis.” European Conference on Object-Oriented Programming (ECOOP), 2021.

Tornede, Tanja, et al. “Towards Green Automated Machine Learning: Status Quo and Future Directions.” ArXiv:2111.05850, 2021.

Kersting, Joschka, and Michaela Geierhos. “Human Language Comprehension in Aspect Phrase Extraction with Importance Weighting.” Natural Language Processing and Information Systems, edited by Epaminondas Kapetanios et al., vol. 12801, Springer, 2021, pp. 231--242.

Pauck, Felix, and Heike Wehrheim. “Jicer: Simplifying Cooperative Android App Analysis Tasks.”10.1109/scam52516.2021.00031__2021 IEEE 21st International Working Conference on Source Code Analysis and Manipulation (SCAM), 2021, doi:10.1109/scam52516.2021.00031.

Alshomary, Milad, et al. “Belief-Based Generation of Argumentative Claims.” Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, Association for Computational Linguistics, 2021, pp. 224–223.

Chen, Wei-Fan, et al. “Controlled Neural Sentence-Level Reframing of News Articles.” Findings of the Association for Computational Linguistics: EMNLP 2021, 2021, pp. 2683–93.

Alshomary, Milad, et al. “Argument Undermining: Counter-Argument Generation by Attacking Weak Premises.”10.18653/v1/2021.findings-acl.159__Proceedings of the Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021), Association for Computational Linguistics, 2021, pp. 1816–1827, doi:10.18653/v1/2021.findings-acl.159.

Hanselle, Jonas Manuel, et al. Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data. 2021.

Jakobs, Marie-Christine, et al. “Software/Hardware Co-Verification for Custom Instruction Set Processors.”10.1109/ACCESS.2021.3131213__IEEE Access, IEEE, 2021, doi:10.1109/ACCESS.2021.3131213.

Pauck, Felix, and Heike Wehrheim. “Cooperative Android App Analysis with CoDiDroid.”10.18420/SE2021_30 __Software Engineering 2021, edited by Anne Koziolek et al., Gesellschaft für Informatik e.V., 2021, pp. 83–84, doi:10.18420/SE2021_30 .

Ahmed, Qazi Arbab. “Hardware Trojans in Reconfigurable Computing.”10.1109/vlsi-soc53125.2021.9606974__2021 IFIP/IEEE 29th International Conference on Very Large Scale Integration (VLSI-SoC), 2021, doi:10.1109/vlsi-soc53125.2021.9606974.

Ahmed, Qazi Arbab, et al. “Malicious Routing: Circumventing Bitstream-Level Verification for FPGAs.”10.23919/DATE51398.2021.9474026__2021 Design, Automation &; Test in Europe Conference &; Exhibition (DATE), 2021 Design, Automation and Test in Europe Conference (DATE), 2021, doi:10.23919/DATE51398.2021.9474026.

Schubert, Philipp, et al. “Into the Woods: Experiences from Building a Dataflow Analysis Framework for C/C++.” SCAM ’21: IEEE International Working Conference on Source Code Analysis and Manipulation (Engineering Track), 2021.

Schubert, Philipp, et al. “Modeling the Effects of Global Variables in Data-Flow Analysis for C/C++.” 2021 IEEE 21st International Working Conference on Source Code Analysis and Manipulation (SCAM), 2021.

Sharma, Arnab, and Heike Wehrheim. “Automatic Fairness Testing of Machine Learning Models.” Proceedings of the 32th IFIP International Conference on Testing Software and Systems (ICTSS), Springer.

Bäumer, Frederik Simon, et al. “Tag Me If You Can: Insights into the Challenges of Supporting Unrestricted P News Tagging.”https://doi.org/10.1007/978-3-030-59506-7_30__Information and Software Technologies, edited by Lopata Audrius et al., vol. 1283, Springer, 2020, pp. 368--382, doi:https://doi.org/10.1007/978-3-030-59506-7_30.

Witschen, Linus Matthias, et al. “Proof-Carrying Approximate Circuits.”10.1109/TVLSI.2020.3008061__IEEE Transactions On Very Large Scale Integration Systems, vol. 28, no. 9, IEEE, 2020, pp. 2084–88, doi:10.1109/TVLSI.2020.3008061.

Tornede, Alexander, et al. “Extreme Algorithm Selection with Dyadic Feature Representation.” Discovery Science, 2020.

Hanselle, Jonas Manuel, et al. “Hybrid Ranking and Regression for Algorithm Selection.” KI 2020: Advances in Artificial Intelligence, 2020.

Tornede, Tanja, et al. “AutoML for Predictive Maintenance: One Tool to RUL Them All.”10.1007/978-3-030-66770-2_8__Proceedings of the ECMLPKDD 2020, 2020, doi:10.1007/978-3-030-66770-2_8.

Kersting, Joschka, and Frederik Simon Bäumer. “SEMANTIC TAGGING OF REQUIREMENT DESCRIPTIONS: A TRANSFORMER-BASED APPROACH.” PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON APPLIED COMPUTING 2020, IADIS, 2020, pp. 119--123.

Tornede, Alexander, et al. “Towards Meta-Algorithm Selection.” Workshop MetaLearn 2020 @ NeurIPS 2020, 2020.

Schubert, Philipp, et al. Accelerating Static Call-Graph, Points-to and Data-Flow Analysis Through Persisted Summaries. 2020.

Witschen, Linus Matthias, et al. “Search Space Characterization for AxC Synthesis.” Fifth Workshop on Approximate Computing (AxC 2020).

Tornede, Alexander, et al. “Run2Survive: A Decision-Theoretic Approach to Algorithm Selection Based on Survival Analysis.” ACML 2020, 2020.

Sharma, Arnab, and Heike Wehrheim. “Higher Income, Larger Loan? Monotonicity Testing of Machine Learning Models.” Proceedings of the ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA)., ACM.

Richter, Cedric, et al. “Algorithm Selection for Software Validation Based on Graph Kernels.” Journal of Automated Software Engineering, Springer.

Kersting, Joschka, and Michaela Geierhos. “Aspect Phrase Extraction in Sentiment Analysis with Deep Learning.” Proceedings of the 12th International Conference on Agents and Artificial Intelligence (ICAART 2020) --  Special Session on Natural Language Processing in Artificial Intelligence (NLPinAI 2020), SCITEPRESS, 2020, pp. 391--400.

Buff, Bianca, et al. “Detection of Privacy Disclosure in the Medical Domain: A Survey.” Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2020), SCITEPRESS, 2020, pp. 630--637.

Wever, Marcel Dominik, et al. LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification. Springer.

Kersting, Joschka, and Michaela Geierhos. “Neural Learning for Aspect Phrase Extraction and Classification in Sentiment Analysis.” Proceedings of the 33rd International Florida Artificial Intelligence Research Symposium (FLAIRS) Conference, AAAI, 2020, pp. 282--285.

Wever, Marcel Dominik, et al. “Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets.”10.1162/evco_a_00266__Evolutionary Computation, vol. 28, no. 2, MIT Press Journals, 2020, pp. 165–193, doi:10.1162/evco_a_00266.

Kersting, Joschka, and Michaela Geierhos. “What Reviews in Local Online Labour Markets Reveal about the Performance of Multi-Service Providers.” Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods, SCITEPRESS, 2020, pp. 263--272.

Karl, Holger, et al. “A Case for a New IT Ecosystem: On-The-Fly Computing.”10.1007/s12599-019-00627-x__Business &; Information Systems Engineering, vol. 62, no. 6, Springer, 2020, pp. 467–81, doi:10.1007/s12599-019-00627-x.

Chen, Wei-Fan, et al. “Analyzing Political Bias and Unfairness in News Articles at Different Levels of Granularity.” Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, 2020, pp. 149–54.

Syed, Shahbaz, et al. “Task Proposal: Abstractive Snippet Generation for Web Pages.” Proceedings of the 13th International Conference on Natural Language Generation (INLG 2020), 2020, pp. 237–41.

Chen, Wei-Fan, et al. “Detecting Media Bias in News Articles Using Gaussian Bias Distributions.” Findings of the Association for Computational Linguistics: EMNLP 2020, 2020, pp. 4290–300.

Chen, Wei-Fan, et al. “Abstractive Snippet Generation.” Proceedings of the Web Conference 2020, 2020, pp. 1309–19.

Alshomary, Milad, et al. “Target Inference in Argument Conclusion Generation.” Proceedings of 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), Association for Computational Linguistics, 2020, pp. 4334–45.

Schneider, Stefan Balthasar, et al. “Specifying and Analyzing Virtual Network Services Using Queuing Petri Nets.” 2019 IFIP/IEEE International Symposium on Integrated Network Management (IM), IFIP, 2019, pp. 116--124.

Witschen, Linus Matthias, et al. “CIRCA: Towards a Modular and Extensible Framework for Approximate Circuit Generation.”10.1016/j.microrel.2019.04.003__Microelectronics Reliability, vol. 99, Elsevier, 2019, pp. 277–90, doi:10.1016/j.microrel.2019.04.003.

Sharma, Arnab, and Heike Wehrheim. “Testing Balancedness of ML Algorithms.” Proceedings of the Software Engineering Conference (SE), vol. P-292, Gesellschaft für Informatik e.V. (GI), 2019, pp. 157–58.

Bäumer, Frederik Simon, and Michaela Geierhos. “Requirements Engineering in OTF-Computing.” Encyclopedia.Pub, MDPI, 2019.

Sharma, Arnab, and Heike Wehrheim. “Testing Machine Learning Algorithms for Balanced Data Usage.” IEEE International Conference on Software Testing, Verification and Validation (ICST), IEEE, 2019, pp. 125--135.

Bäumer, Frederik Simon, et al. “Natural Language Processing in OTF Computing: Challenges and the Need for Interactive Approaches.”10.3390/computers8010022__Computers, vol. 8, no. 1, 22, MDPI AG, Basel, Switzerland, 2019, doi:10.3390/computers8010022.

Wever, Marcel Dominik, et al. Towards Automated Machine Learning for Multi-Label Classification. 2019.

Pauck, Felix, and Shikun Zhang. “Android App Merging for Benchmark Speed-Up and Analysis Lift-Up.”10.1109/asew.2019.00019__2019 34th IEEE/ACM International Conference on Automated Software Engineering Workshop (ASEW), 2019, doi:10.1109/asew.2019.00019.

Dann, Andreas, et al. “ModGuard: Identifying Integrity &;Confidentiality Violations in Java Modules.”10.1109/tse.2019.2931331__IEEE Transactions on Software Engineering, 2019, pp. 1–1, doi:10.1109/tse.2019.2931331.

Melnikov, Vitaly, and Eyke Hüllermeier. “Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA.”10.1016/j.jmva.2019.02.017__Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101), 2019, doi:10.1016/j.jmva.2019.02.017.

Tornede, Alexander, et al. “Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking.” Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019, edited by Frank Hoffmann et al., KIT Scientific Publishing, Karlsruhe, 2019, pp. 135–46.

Mohr, Felix, et al. “From Automated to On-The-Fly Machine Learning.” INFORMATIK 2019: 50 Jahre Gesellschaft Für Informatik – Informatik Für Gesellschaft, Gesellschaft für Informatik e.V., 2019, pp. 273–74.

Beyer, Dirk, et al. “Combining Verifiers in Conditional Model Checking via Reducers.”10.18420/se2019-46__Software Engineering and Software Management (SE/SWM 2019), Stuttgart, Germany, February 18-22, 2019, edited by Steffen Becker et al., vol. P-292, GI, 2019, pp. 151--152, doi:10.18420/se2019-46.

Richter, Cedric, and Heike Wehrheim. “PeSCo: Predicting Sequential Combinations of Verifiers - (Competition Contribution).”10.1007/978-3-030-17502-3_19__Tools and Algorithms for the Construction and Analysis of Systems - 25 Years of TACAS: TOOLympics, Held as Part of ETAPS 2019, Prague, Czech Republic, April 6-11, 2019, Proceedings, Part III, edited by Dirk Beyer et al., vol. 11429, Springer, 2019, pp. 229–33, doi:10.1007/978-3-030-17502-3_19.

Wever, Marcel Dominik, et al. Automating Multi-Label Classification Extending ML-Plan. 2019.

Schubert, Philipp, et al. “PhASAR: An Inter-Procedural Static Analysis Framework for C/C++.”10.1007/978-3-030-17465-1_22__Proceedings of the 25th International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS 2019), Held as Part of the European Joint Conferences on Theory and Practice of Software (ETAPS 2019), vol. II, 2019, pp. 393–410, doi:10.1007/978-3-030-17465-1_22.

Chen, Wei-Fan, et al. “Unraveling the Search Space of Abusive Language in Wikipedia with Dynamic Lexicon Acquisition.” Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom, 2019, pp. 76–82.

Pauck, Felix, and Heike Wehrheim. “Together Strong: Cooperative Android App Analysis.”10.1145/3338906.3338915__Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 2019, pp. 374–84, doi:10.1145/3338906.3338915.

Isenberg, Tobias, et al. “When Are Software Verification Results Valid for Approximate Hardware?”10.1007/978-3-030-31157-5_1__Tests and Proofs - 13th International Conference, TAP 2019, Held as Part of the Third World Congress on Formal Methods 2019, Porto, Portugal, October 9-11, 2019, Proceedings, 2019, pp. 3–20, doi:10.1007/978-3-030-31157-5_1.

Ahmed, Qazi Arbab, et al. “Proof-Carrying Hardware Versus the Stealthy Malicious LUT Hardware Trojan.”10.1007/978-3-030-17227-5_10__Applied Reconfigurable Computing, edited by Christian Hochberger et al., vol. 11444, Springer International Publishing, 2019, pp. 127–36, doi:10.1007/978-3-030-17227-5_10.

Schubert, Philipp, et al. “Know Your Analysis: How Instrumentation Aids Understanding Static Analysis.”10.1145/3315568.3329965__Proceedings of the 8th ACM SIGPLAN International Workshop on State Of the Art in Program Analysis  - SOAP 2019, 2019, doi:10.1145/3315568.3329965.

Mohr, Felix, et al. “(WIP) Towards the Automated Composition of Machine Learning Services.”10.1109/SCC.2018.00039__SCC, IEEE, 2018, doi:10.1109/SCC.2018.00039.

Mohr, Felix, et al. “Programmatic Task Network Planning.” Proceedings of the 1st ICAPS Workshop on Hierarchical Planning, AAAI, 2018, pp. 31–39.