@inproceedings{HanenbergEtAl_Indentation_JSON_2024,

title = {Indentation and Reading Time: A Controlled Experiment on the Differences Between Generated Indented and Non-indented JSON Objects},

author = {Stefan Hanenberg and Johannes Morzeck and Ole Werger and Stefan Gries and Volker Gruhn},

editor = {Hans-Georg Fill and Francisco José Domínguez Mayo and Marten Sinderen and Leszek A. Maciaszek},

isbn = {978-3-031-61753-9},

year  = {2024},

date = {2024-01-01},

booktitle = {Software Technologies},

pages = {50–75},

publisher = {Springer Nature Switzerland},

address = {Cham},

abstract = {It is commonly accepted to indent source code to improve its readability, but the evidence for that is quite low. Up to 2022 only few experiments can be found that reveal a positive effect of indentation in terms of reported evidence and reported effect size – actually, the authors of the present paper are only aware of one single experiment up to 2022 that matches the previous statement. In other words, the often articulated positive effect of indentation was hardly backed up by experimental results reported in the literature. The situation changed in 2023, where an experiment suddenly revealed a strong (p $$< .001$$<.001) and large (effect size = .832; ratio of means = 2.781) effect of indentation in code consisting of if-statements, i.e., a large effect of indentation on the readability of control flows was measured. However, taking the history of studies on indentation into account, it is reasonable to doubt the results: possibly, the effect was caused by hand-chosen examples, respectively by the kind of code used in the experiment (control flows). The here presented study is a follow-up study. First, instead of using hand-chosen examples, the experiment uses generated code. Next, instead of relying on an application's control flow, the present study focusses on data structures by using JSON objects. And finally, the study was designed as a replicated N-of-1 experiment executed on 40 professional developers, where each single subject receives multiple repetitions of all treatment combinations. The study showed a strong (p < .001) and large (effect size = .596; ratio of means = 6.44) effect of indentation. A further finding was that the indentation effect varies between participants. And finally, the study's results are comparable to the results achieved by running the experiment only on four of the present paper's authors.},

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{Syniawa:2024:ExploringLLMsInIndustrialRobotics,

title = {Exploring Large Language Models in Industrial Robotics: Interacting with Simulation Systems and Validating AI-Generated Robot Code},

author = {Daniel Syniawa and Marius Boshoff and Bernd Kuhlenkötter and Darko Katic and David Schuster and Marc Hesenius},

year  = {2024},

date = {2024-01-01},

booktitle = {Automation 2024},

publisher = {VDI Verlag},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Hermann:2024:DroneGesturesAgeDifferences,

title = {Age-Based Differences in Drone Control Gestures: An Exploratory Study},

author = {Julia Hermann and Aysegül Dogangün and Marc Hesenius},

doi = {10.1145/3638380.3638401},

year  = {2024},

date = {2024-01-01},

booktitle = {Proceedings of the 35th Australian Computer-Human Interaction Conference},

pages = {49–58},

publisher = {Association for Computing Machinery},

address = {Wellington, New Zealand},

series = {OzCHI '23},

abstract = {In addition to cultural background and professional context, age can also influence user preferences for gestures when controlling drones. In this paper, we present an exploratory study of age-based differences in gesture selection and design for drone control with 24 participants aged 18 to 69. We investigated the impact of age on the shape, type, body parts used, and number of partial gestures employed for drone direction and interaction commands. Firstly, we quantified the number and type of gestures utilized by each age group for specific commands, revealing significant disparities between older and younger participants. Additionally, we explore the qualitative aspects of gesture control by analyzing participants' descriptions and verbalized thoughts, uncovering further differences and similarities. Overall, our paper contributes to the growing body of research on Human-Drone Interaction by highlighting the significance of age in gesture control and its consequences for the individual User Experience. By incorporating these findings into future design practices, we can advance the field and promote inclusive drone interfaces that accommodate users of all ages.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Bachert:2024:TowardsSynthGestureEval,

title = {Towards a Framework for Evaluating Synthetic Surface Gestures},

author = {Marvin Bachert and Marc Hesenius},

doi = {10.1145/3660515.3661327},

year  = {2024},

date = {2024-01-01},

booktitle = {Companion Proceedings of the 16th ACM SIGCHI Symposium on Engineering Interactive Computing Systems},

publisher = {ACM},

address = {Cagliari, Italy},

series = {EICS Companion '24},

abstract = {Synthetic surface gestures are required for efficient test-automation of gesture-based applications, but must resemble human gestures as closely as possible. We thus require options to assess the human-like quality of synthetic surface gestures. In this paper, we discuss several approaches for evaluating synthetic data from other fields and map them to synthetic surface gestures. We also propose a framework to assess a gesture generator's performance and evaluate its feasibility in a preliminary evaluation with three different gestures. Our results indicate that the proposed framework is able to rate different datasets according to expectations of similarity to a baseline dataset.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Hesenius:2024:DoesMyDataFit,

title = {Does My Data Fit? Assessing the Compatibility Between New and Existing Data},

author = {Marc Hesenius and Marvin Bachert},

doi = {10.1145/3660515.3662840},

year  = {2024},

date = {2024-01-01},

booktitle = {Companion Proceedings of the 16th ACM SIGCHI Symposium on Engineering Interactive Computing Systems},

publisher = {ACM},

address = {Cagliari, Italy},

series = {EICS Companion '24},

abstract = {With the widespread use of Machine Learning within interactive applications, high-quality data is key to ensuring an acceptable User Experience. This applies to training data as well as runtime data used for inference. Also, with the increasing demand for training data, data synthesis is interesting, but synthetic data must be similar to real data. Software Engineers thus need methods to assess data quality. This tutorial equips participants with techniques to check whether new data aligns with existing data. We offer practical insights and techniques to address the challenges in validating synthetic data, e.g., for test automation, and verifying if runtime data conforms to training data. Focusing on gesture recognition, participants will learn to differentiate between data suitable for the trained gesture recognizer and data that will potentially yield wrong results and thus lead to an undesirable User Experience.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@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}

}