Kallistos Weis

Marvin Wyrich, Norman Peitek, Kallistos Weis et al.

Software engineering students often struggle to appreciate empirical methods and hypothesis-driven inquiry, especially when taught in theoretical terms. This experience report explores whether grounding empirical learning in hype-driven technologies can make these concepts more accessible and engaging. We conducted a one-semester seminar framed around the currently popular topic of AI coding assistants, which attracted unusually high student interest. The course combined hands-on sessions using AI coding assistants with small, student-designed empirical studies. Classroom observations and survey responses suggest that the hype topic sparked curiosity and critical thinking. Students engaged with the AI coding assistants while questioning their limitations -- developing the kind of empirical thinking needed to assess claims about emerging technologies. Key lessons: (1) Hype-driven topics can lower barriers to abstract concepts like empirical research; (2) authentic hands-on development tasks combined with ownership of inquiry foster critical engagement; and (3) a single seminar can effectively teach both technical and research skills.

Clemens Dubslaff, Kallistos Weis, Christel Baier et al.

Detecting and understanding reasons for defects and inadvertent behavior in software is challenging due to their increasing complexity. In configurable software systems, the combinatorics that arises from the multitude of features a user might select from adds a further layer of complexity. We introduce the notion of feature causality, which is based on counterfactual reasoning and inspired by the seminal definition of actual causality by Halpern and Pearl. Feature causality operates at the level of system configurations and is capable of identifying features and their interactions that are the reason for emerging functional and non-functional properties. We present various methods to explicate these reasons, in particular well-established notions of responsibility and blame that we extend to the feature-oriented setting. Establishing a close connection of feature causality to prime implicants, we provide algorithms to effectively compute feature causes and causal explications. By means of an evaluation on a wide range of configurable software systems, including community benchmarks and real-world systems, we demonstrate the feasibility of our approach: We illustrate how our notion of causality facilitates to identify root causes, estimate the effects of features, and detect feature interactions.