GenAI Supported Software Engineering Tasks (AISeTa)

GenAI has the potential to improve the quality of artifacts in cooperation with experienced software engineers efficiently. Due to the generic nature of GenAI a hybrid approach should be investigated for all major artifacts (requirements, design, code, unit tests, acceptance tests). The quality focus (e.g., evolvability, green code) can be easily shifted. In a second work package, software evolution with LLMs is addressed. The evolution of existing (legacy) software is a major challenge. Supported by GenAI systems this task could be more manageable. Experience with a value-based migration of parts of MUSE from Perl to Python are promising. Currently, there are some method frameworks available how to evolve software – these approaches do not consider the potential of GenAI systems and therefore have to be adopted. As a goal, a method toolbox should be provided for the various tasks necessary for software evolution.