Jennifer Brings

As software becomes increasingly important, so does the education of people who develop software. To provide a good and motivating education to learners, it is necessary to be aware of their preferences. To give insight into the preference of learners, we conducted a study with university students and professionals aimed at learning more about their preferences. Results from our survey- and focus group-based research show large similarities between both groups of learners, such as the preference for e-learning materials, as this provides them flexibility for scheduling their learning sessions. However, differences exist when it comes to the preferred scheduling of online and offline sessions. Our findings can help educators not only in designing courses for university students or professionals, but also in designing courses that can be used by both groups of learners.

This position paper discusses the potential for using generative AIs like ChatGPT in software engineering education. Currently, discussions center around potential threats emerging from student's use of ChatGPT. For instance, generative AI will limit the usefulness of graded homework dramatically. However, there exist potential opportunities as well. For example, ChatGPT's ability to understand and generate human language allows providing personalized feedback to students, and can thus accompany current software engineering education approaches. This paper highlights the potential for enhancing software engineering education. The availability of generative AI will improve the individualization of education approaches. In addition, we discuss the need to adapt software engineering curricula to the changed profiles of software engineers. Moreover, we point out why it is important to provide guidance for using generative AI and, thus, integrate it in courses rather than accepting the unsupervised use by students, which can negatively impact the students' learning.

One central aspect of software engineering research is the transfer of the proposed approaches into industrial practice. In the past, a number of technology transfer approaches and experiences from technology transfer projects in software engineering have already been reported. However, many researchers still struggle to get their research results noticed by practitioners. To investigate what is important to practitioners, we conducted a mixed‐methods study that provides us with reliable quantitative data as well as deeper insights from qualitative data. Our results show that there is a mismatch between industry professionals' needs and commonly proposed technology transfer approaches in the software engineering field. For instance, collaboration between industry and academia as well as participation in empirical evaluations is not deemed important from an industry point of view. In contrast, industry professionals emphasize the use of company‐specific pilot projects conducted by industry and the need for experts to be available in every phase of technology transfer.

Model inspections are important to ensure high‐quality software and to satisfy legal obligations in model‐based engineering processes. As model‐based specifications are typically documented on type‐level, errors concerning the interactions between multiple system instances can go unnoticed. For collaborative cyber‐physical systems (CPS), a plethora of possible instance‐level configurations need to be taken into account. Therefore, we propose the definition of instance‐level review diagrams that show representative interactions of instance‐level configurations that help detect defects in the system specification. To evaluate the approach, we conducted a controlled experiment whose results indicate that instance‐level review diagrams have—compared with type‐level diagrams—important positive effects on reviewing processes for behavioral specifications of CPS. Specifically, the experiment provides empirical evidence that instance‐level review diagrams are significantly more expressive and effective than type‐level diagrams.

Students’ experience is used in empirical software engineering research as well as in software engineering education to group students in either homogeneous or heterogeneous groups. To do so, students are commonly asked to self-rate their experience, as self-rated experience has been shown to be a good predictor for performance in programming tasks. Another experience-related measurement is participants’ confidence (i.e., how confident is the person that their given answer is correct). Hence, self-rated experience and confidence are used as selector or control variables throughout empirical software engineering research and software engineering education. In this paper, we analyze data from several student experiments conducted in the past years to investigate whether self-rated experience and confidence are also good predictors for students’ performance in model comprehension tasks. Our results show that while students can somewhat assess the correctness of a particular answer to one concrete question regarding a conceptual model (i.e., their confidence), their overall self-rated experience does not correlate with their actual performance. Hence, the use of the commonly used measurement of self-rated experience as a selector or control variable must be considered unreliable for model comprehension tasks.

Collaborative cyber-physical systems are capable of forming networks at runtime to achieve goals that are unachievable for individual systems. They do so by connecting to each other and exchanging information that helps them coordinate their behaviors to achieve shared goals. Their highly complex dependencies, however, are difficult to document using traditional goal modeling approaches. To help developers of collaborative cyber-physical systems leverage the advantages of goal modeling approaches, we developed a GRL-compliant extension to the popular iStar goal modeling language that takes the particularities of collaborative cyber-physical systems and their developers’ needs into account. In particular, our extension provides support for explicitly distinguishing between the goals of the individual collaborative cyber-physical systems and the network and for documenting various dependencies not only among the individual collaborative cyber-physical systems but also between the individual systems and the network. We provide abstract syntax, concrete syntax, and well-formedness rules for the extension. To illustrate the benefits of our extension for goal modeling of collaborative cyber-physical systems, we report on two case studies conducted in different industry domains.