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Auflistung nach Autor:in "Pietsch, Christopher"

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  • Konferenzbeitrag
    A Formal Framework for Incremental Model Slicing
    (Software Engineering and Software Management 2019, 2019) Taentzer, Gabriele; Kehrer, Timo; Pietsch, Christopher; Kelter, Udo
    We report about a recently developed “Formal Framework for Incremental Model Slicing”, published in [Ta18]. A model slice of a model is a submodel comprising a selected model part, called slicing criterion. In addition to classical use cases from the field of program understanding, model slicing is also motivated by specifying submodels of interest to be further processed more efficiently. Since slicing criteria are often modified during software development tasks, such slices often need to be updated. A slice update can be performed by creating the new slice from scratch or by incrementally updating the existing slice. We present a formal framework for defining model slicers that support incremental slice updates. This framework abstracts from the behavior of concrete slicers as well as from the concrete model modification approach. Incremental slice updates are shown to be equivalent to non-incremental ones. Furthermore, we present a framework instantiation based on the concept of edit scripts defining application sequences of model transformation rules, along with two two concrete model slicers implemented based on this instantiation.
  • Konferenzbeitrag
    A Summary of ReVision: History-based Model Repair Recommendations
    (Software Engineering 2023, 2023) Ohrndorf, Manuel; Pietsch, Christopher; Kelter, Udo; Grunske, Lars; Kehrer, Timo
    This work reports recent research results on history-based model repair recommendations in Model-Driven Engineering (MDE), originally published in Reference [Oh21]. Models in MDE are primary development artifacts that are heavily edited in all software development stages and can become temporarily inconsistent during editing. Model repair tools can support developers by proposing a list of the most promising repairs. Such repair recommendations will only be accepted in practice if the generated proposals are plausible and understandable and the set as a whole is manageable. Our interactive repair tool ReVision [Oh18], aims at generating repair proposals for inconsistencies introduced by past incomplete edit steps. Such an incomplete edit step is either undone or extended to the full execution of a consistency-preserving edit operation. We evaluate our approach using histories of real-world models from popular open-source modeling projects. Our experimental results confirm our hypothesis that most of the inconsistencies can be resolved by complementing incomplete edits. In fact, 92.2% of the proposed complementations could be observed in the model history.
  • Konferenzbeitrag
    Transferscope – Making Multi-Modal Conditioning for Image Diffusion Models Tangible
    (Mensch und Computer 2024 - Workshopband, 2024) Pietsch, Christopher; Stankowski, Aeneas
    The significance of artificial intelligence (AI) is progressively amplifying for designers, especially within the domain of human-computer interaction. For design students, a foundational comprehension of machine learning (ML) algorithms is indispensable to navigate and utilize this technology in both theoretical and applied contexts - in order to leverage it within design proposals, and also within the design process. Generative AI Tools have rapidly entered creative processes of designers and artists alike, and have been heavily adopted by lay people. They have been praised for democratizing high-quality image creation. However, there are still concerns about the limited artistic control and steerability they provide , especially for professional creatives. This raises questions about how well these tools can be integrated into carefully developed creative workflows, given the constraints on composition and detail. Additionally, text2image algorithms are highly competitive with more manual creation and visualisation techniques in terms of speed and fidelity, while lacking opportunities for deliberation and fine-grained control. As a physical artifact, Transferscope attempts to tangibly introduce professional designers and students to generative AI powered workflows that facilitate creative control, while maintaining the option to leverage serendipity-driven iteration uniquely made possible by the instant-availability provide by image generation models like stable diffusion.Transferscope serves an educational purposes within an experiential teaching approach, and has been designed to work within exhibition and classroom settings alike.