@inproceedings{Severin:2024:DataRobustness,

title = {Time-Shift Robustness Evaluation for Applications using Artificial Intelligence},

author = {Benedikt Severin and Maria Keil and Arnd Meiser and Sarah Straub and Caroline Ruiner and Vera Hagemann and Matthias Klumpp and Marc Hesenius},

url = {https://aisel.aisnet.org/ecis2024/track03_ai/track03_ai/2},

year  = {2024},

date = {2024-01-01},

booktitle = {European Conference on Information Systems. ECIS 2024 Proceedings. 2.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Severin:2024:OODTests,

title = {Not What I was Trained for – Out-of-Distribution-Tests for Interactive AIs},

author = {Benedikt Severin and Ole Werger and Marc Hesenius},

editor = {Michael Harrison and Célia Martinie and Nicholas Micallef and Philippe Palanque and Albrecht Schmidt and Marco Winckler and Enes Yigitbas and Luciana Zaina},

isbn = {978-3-031-59235-5},

year  = {2024},

date = {2024-01-01},

booktitle = {Engineering Interactive Computer Systems. EICS 2023 International Workshops and Doctoral Consortium},

pages = {127–147},

publisher = {Springer Nature Switzerland},

address = {Cham},

abstract = {Recent advances in AI demonstrate its capacities to not only automate more and more everyday tasks, but also make direct Human-AI-Interaction possible. When using AI models in production, we might face situations where the AI is confronted with input data that is very different from the data it was trained on. Such situations are called Out-of-Distribution situations and can result in misleading AI inferences. We argue that identification, handling, and prevention of Out-of-Distribution situations is key for creating production-ready interactive AI components. In this paper, we test the robustness of state-of-the-art AI/ML approaches in Out-of-Distribution situations and propose a research agenda to gather a deeper understanding of how to identify, handle, and prevent such situations in interactive applications.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3617572.3617880,

title = {Towards Assessing the Real-World Impact of Defects in Blockchain-Based Smart Contracts},

author = { Michael Hettmer and Benedikt Severin and Florian Blum and Volker Gruhn},

url = {https://doi.org/10.1145/3617572.3617880},

doi = {10.1145/3617572.3617880},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {1st International Workshop on Software Defect Datasets, SDD@ESEC/FSE 2023, San Francisco, USA},

pages = {6–10},

series = {SDD 2023},

abstract = {Blockchain-based smart contracts enable a wide range of use cases by relying on trustless and guaranteed code execution. The immutability of blockchains means that defects in smart contracts cannot be fixed once the code has been deployed. Previous research has focused on identifying defects and developing tools to improve defect detection. However, several existing blockchain defect datasets rely solely on expert knowledge or static code analysis. They do not provide an objective assessment of the real-world impact of defects. We show how blockchain transactions and execution traces can support an objective evaluation of the impact of a smart contract defect. As a foundation for future research, we propose three novel blockchain-specific metrics. Using the defect dataset SmartBugs, we demonstrate the computation of the metrics and the use cases that can benefit from them. This example focuses on five distinct types of the commonly occurring reentrancy vulnerability defect as detected by Slither, Manticore and Osiris.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Severin2022,

title = {Smart Money Wasting: Analyzing Gas Cost Drivers of Ethereum Smart Contracts},

author = {Benedikt Severin and Marc Hesenius and Florian Blum and Michael Hettmer and Volker Gruhn},

doi = {10.1109/ICSME55016.2022.00034},

year  = {2022},

date = {2022-12-19},

urldate = {2022-01-01},

booktitle = {2022 IEEE International Conference on Software Maintenance and Evolution (ICSME)},

pages = {293-304},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Klumpp:2022:DrivingBigData,

title = {Driving Big Data --- Integration and Synchronization of Data Sources for Artificial Intelligence Applications with the Example of Truck Driver Work Stress and Strain Analysis},

author = {Matthias Klumpp and Benedikt Severin and Hendrik Lechte and Jannes Menck and Maria Keil and Sarah Straub and Caroline Ruiner and Viola Milke and Vera Hagemann and Marc Hesenius},

url = {https://aisel.aisnet.org/icis2022/data_analytics/data_analytics/3/},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {International Conference on Information Systems (ICIS) 2022 Proceedings},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{9169423,

title = {Building Hybrid DApps using Blockchain Tactics -The Meta-Transaction Example},

author = {Florian Blum and Benedikt Severin and Michael Hettmer and Philipp Hückinghaus and Volker Gruhn},

doi = {10.1109/ICBC48266.2020.9169423},

year  = {2020},

date = {2020-01-01},

booktitle = {2020 IEEE International Conference on Blockchain and Cryptocurrency (ICBC)},

pages = {1-5},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}