Peeß, PhilippBrocker, AnnabellRöpke, RenéSchroeder, UlrikGreubel, AndréStrickroth, SvenStriewe, Michael2023-10-162023-10-162023https://dl.gi.de/handle/20.500.12116/42569As 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.enAutomatic GenerationProgramming ExercisesPythonJupyterLabText/Conference Paper10.18420/abp2023-6