Grischa Liebel

Isabella Graßl, Christopher Lazik, Shalini Chakraborty et al.

Student engagement is critical for effective learning in software modelling, yet fostering motivation and inclusivity remains a challenge. While existing research has focused on modelling tools, notations, and assessment, little attention has been given to how the choice of problem domains and the diversity, relatability, and cultural perspectives they bring shape students' learning experiences. This study explores how problem domains and teaching methods influence motivation, engagement, inclusiveness, and feedback in modelling education. To investigate these dimensions, we conducted parallel surveys with 90 students and 22 educators. Our findings reveal disconnects between educator assumptions and student preferences: Students show greatest motivation for socially relevant domains and prefer choice in selection, while educators overestimate interest in study-related domains. The study identifies how minor design choices can exclude students. Students perceive feedback as meaningful when visibly acted upon. These findings suggest inclusive domain selection is central to student motivation; thus, we recommend student-centred domain selection.

Victoria Jackson, Grischa Liebel, Rafael Prikladnicki et al.

While the use of Large Language Models (LLMs) in programming has been extensively studied, there is limited understanding of how LLMs support collaborative work where creativity plays a central role. Software design, as a collaborative and creative activity, provides a valuable context for exploring the influence of LLMs on creativity. This study investigates how and where creativity naturally emerges when software designers collaborate with an LLM during a design task. In a laboratory setting simulating a workplace environment, 18 pairs of software professionals with design experience were asked to complete a design task. Each pair had 90 minutes to produce a software design based on a set of requirements, with optional access to a custom LLM interface. Pairs were not primed to be creative. We find that creativity was present in all pairs in design processes, with 13 producing design documents containing creativity. We primarily attribute creativity to the human designers, driven by traits such as prior experience, empathy, and the use of analogies. The LLM contributed by producing novel ideas and elaborating human ideas. However, in some cases, the LLM appeared to hinder creativity by suggesting complex solutions or adding to unproductive digressions. LLMs can support creativity in collaborative software design, but human insights remain central. To effectively augment human creativity, designers must be intentional in their engagement with LLMs.

Gabrielle O'Brien, Antonio Pedro Santos Alves, Sebastian Baltes et al.

Programming assistants powered by large language models (LLMs) have become widely available, with conversational assistants like ChatGPT proving particularly accessible to less experienced programmers. However, the varied capabilities of these tools across model versions and the mixed availability of extensions that enable web search, code execution, or retrieval-augmented generation create opportunities for user misconceptions about what systems can and cannot do. Such misconceptions may lead to over-reliance, unproductive practices, or insufficient quality control in LLM-assisted programming. Here, we aim to characterize misconceptions that users of conversational LLM-based assistants may have in programming contexts. Using a two-phase approach, we first brainstorm and catalog user misconceptions that may occur, and then conduct a qualitative analysis to examine whether these conceptual issues surface in naturalistic Python-programming conversations with an LLM-based chatbot drawn from an openly available dataset. Indeed, we see evidence that some users have misplaced expectations about the availability of LLM-based chatbot features like web access, code execution, or non-text output generation. We also see potential evidence for deeper conceptual issues around the scope of information required to debug, validate, and optimize programs. Our findings reinforce the need for designing LLM-based tools that more clearly communicate their programming capabilities to users.

Rodi Jolak, Andreas Wortmann, Grischa Liebel et al.

Context: Designing software is an activity in which software developers think and make design decisions that shape the structure and behavior of software products. Designing software is one of the least understood software engineering activities. In a collaborative design setting, various types of distances can lead to challenges and effects that potentially affect how software is designed. Objective: To contribute to a better understanding of collaborative software design, we investigate how geographic distance affects its design thinking and the creativity of its discussions. Method: To this end, we conducted a multiple-case study exploring the design thinking and creativity of co-located and distributed software developers in a collaborative design setting. Results: Compared to co-located developers, distributed developers spend less time on exploring the problem space, which could be related to different socio-technical challenges, such as lack of awareness and common understanding. Distributed development does not seem to affect the creativity of their activities. Conclusion: Developers engaging in collaborative design need to be aware that problem space exploration is reduced in a distributed setting. Unless distributed teams take compensatory measures, this could adversely affect the development. Regarding the effect distance has on creativity, our results are inconclusive and further studies are needed.

Grischa Liebel, Shalini Chakraborty

Context: Using student subjects in empirical studies has been discussed extensively from a methodological perspective in Software Engineering (SE), but there is a lack of similar discussion surrounding ethical aspects of doing so. As students are in a subordinate relationship to their instructors, such a discussion is needed. Objective: We aim to increase the understanding of practices and perceptions SE researchers have of ethical issues with student participation in empirical studies. Method: We conducted a systematic mapping study of 372 empirical SE studies involving students, following up with a survey answered by 100 SE researchers regarding their current practices and opinions regarding student participation. Results: The mapping study shows that the majority of studies does not report conditions regarding recruitment, voluntariness, compensation, and ethics approval. In contrast, the majority of survey participants supports reporting these conditions. The survey further reveals that less than half of the participants require ethics approval. Additionally, the majority of participants recruit their own students on a voluntary basis, and use informed consent with withdrawal options. There is disagreement among the participants whether course instructors should be involved in research studies and if should know who participates in a study. Conclusions: It is a positive sign that mandatory participation is rare, and that informed consent and withdrawal options are standard. However, we see immediate need for action, as study conditions are under-reported, and as opinions on ethical practices differ widely. In particular, there is little regard in SE on the power relationship between instructors and students.

Eric Knauss, Grischa Liebel, Jennifer Horkoff et al.

T-Reqs is a text-based requirements management solution based on the git version control system. It combines useful conventions, templates and helper scripts with powerful existing solutions from the git ecosystem and provides a working solution to address some known requirements engineering challenges in large-scale agile system development. Specifically, it allows agile cross-functional teams to be aware of requirements at system level and enables them to efficiently propose updates to those requirements. Based on our experience with T-Reqs, we i) relate known requirements challenges of large-scale agile system development to tool support; ii) list key requirements for tooling in such a context; and iii) propose concrete solutions for challenges.