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Auflistung nach Schlagwort "JupyterLab"

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  • Konferenzbeitrag
    A Grammar and Parameterization-Based Generator for Python Programming Exercises
    (Proceedings of the Sixth Workshop "Automatische Bewertung von Programmieraufgaben" (ABP 2023), 2023) Peeß, Philipp; Brocker, Annabell; Röpke, René; Schroeder, Ulrik
    As the importance of programming education grows, the demand for a sufficient number of practical exercises in courses also increases. To accommodate this need without significantly increasing the instructors' workload, a programming exercise generator capable of generating exercises for independent practice is considered. This research mainly focuses on determining suitable generation methods and creating a modular and extensible generator structure. The current generator implementation uses parameterization and a grammar-based generation approach in order to provide generated exercises directly to students in their programming environment. Furthermore, the generator can act as a foundation for further research and be extended with additional generation methods, creating the possibility of exploring artificial intelligence for the generation of programming exercises.
  • Konferenzbeitrag
    MLProvCodeGen: A Tool for Provenance Data Input and Capture of Customizable Machine Learning Scripts
    (BTW 2023, 2023) Mustafa, Tarek Al; König-Ries, Birgitta; Samuel, Sheeba
    Over the last decade Machine learning (ML) has dramatically changed the application ofand research in computer science. It becomes increasingly complicated to assure the transparency and reproducibility of advanced ML systems from raw data to deployment. In this paper, we describe an approach to supply users with an interface to specify a variety of parameters that together provide complete provenance information and automatically generate executable ML code from this information. We introduce MLProvCodeGen (Machine Learning Provenance Code Generator), a JupyterLab extension to generate custom code for ML experiments from user-defined metadata. ML workflows can be generated with different data settings, model parameters, methods, and trainingparameters and reproduce results in Jupyter Notebooks. We evaluated our approach with two ML applications, image and multiclass classification, and conducted a user evaluation.
  • Konferenzbeitrag
    Teaching the Use and Engineering of DSLs with JupyterLab: Experiences and Lessons Learned
    (Modellierung 2022, 2022) Charles, Joel; Jansen, Nico; Michael, Judith; Rumpe, Bernhard
    Domain-Specific Languages (DSLs) are tailored to a specific domain which requires them to provide domain-specific concepts and a sophisticated tooling for their engineering; aspects which we address with the language workbench MontiCore. As we use MontiCore for research and teaching, we are interested in reducing the entry barrier to use and engineer MontiCore DSLs. While there are approaches for ready-to-use learning environments such as web-based editors, only a few provide a tailored solution for specific DSLs. Within this paper, we present our experiences using JupyterLab in combination with the infrastructure of MontiCore for teaching the use and engineering of DSLs in an interactive manner. We have realized three practical courses and one conference tutorial applying this technical approach. The front-end provides immediate feedback and includes supporting explanations in an integrated manner. Initial feedback indicates that this approach can lower the entry barrier for DSL use and engineering for students and practitioners.