Software Engineering for Artificial Intelligence

Data-driven artificial intelligence (AI) solutions are being adopted in many areas, including finance, medicine, cognitive sciences, and biology. Such machine learning (ML) approaches require an accurate proper software design and development, dedicated testing and debugging, as well as specific techniques that ensure scalability and maintainability. While AI-enabled systems continue to have a tremendous impact on many fields, developers and data scientists still follow methods (scripting, informal/non-written specifications, trial-and-error testing) that do not conform to the state of the art of engineering disciplines. In this context, it is of paramount importance to take advantage of the decades-long developments of software engineering (SE), and adapt them to systematize the development process of ML solutions. Also, developers using today’s AI technology in their projects face novel software engineering challenges requiring adaptations.

With a group of max. 8 participants current scientific work from the area of applying software engineering for AI is to be analyzed under guidance of the lecturer firsts. Then improvements are to be worked out, which are to be written down as a group in form of a new work. Details on the topic will be announced in the first event.

Learning Outcomes

The students develop a deeper understanding of software engineering for artificial intelligence. The students learn to work with scientific contents and to develop new research ideas expanding on previous work. This involves literature research and scientific writing practice. The students develop the ability to collaborate on scientific writing.


This seminar is provided to you by the Software Technology Group of Technical University of Darmstadt.