Collaboration and Communication (CoCo) among team members while building ML-enabled systems are crucial for integrating diverse expertise. Building an ML-enabled system by practitioners from interdisciplinary fields led to the need for CoCo between experts. This collaborative environment fosters innovation and ensures that the system works as intended. Many papers have recently identified challenges and recommendations for building ML-enabled systems among different team members, but there is a lack of implementation of these recommendations to assess their real effectiveness. Therefore, we will address these problems by implementing our approaches and assisting teams in choosing the most effective techniques for their context.

The world of software development has fundamentally changed because of the explosive growth of opensource repositories in recent years. Open-source repositories have become a valuable tool for software developers and researchers because they are free and usually easy to use. Likewise, learning Artificial Intelligence (AI) and Machine Learning (ML) skills are in high demand, especially among software engineering students, as they increasingly require AI skills to drive innovation, solve complex problems, and remain competitive. There are several AI/ML open-source projects that contain code explanations, e.g., comments and/or documentation, making them potential educational tools. However, it is currently unclear how well AI novices can benefit from these resources. Hence, we studied how computer science bachelor students perceive self-study with open-source repositories to build more complex AI/ML systems to gauge the usefulness of these repositories. After a learning period, we surveyed the perception and learning outcomes from the viewpoint of 112 students. By analyzing the responses, we found that 75 % of the students stated that they could now build complex AI/ML systems if provided with enough documentation and descriptions and are motivated to work on them. While this indicates that learning or improving AI/ML skills via open-source repositories is promising, more research beyond self-reporting is needed.

Abstract A Software Product Line (SPL) is a software development paradigm in which a family of software products shares a set of core assets. Testing has a vital role in both single-system development and SPL development in identifying potential faults by examining the behavior of a product or products, but it is especially challenging in SPL. There have been many research contributions in the SPL testing field; therefore, assessing the current state of research and practice is necessary to understand the progress in testing practices and to identify the gap between required techniques and existing approaches. This paper aims to survey existing research on SPL testing to provide researchers and practitioners with up-to-date evidence and issues that enable further development of the field. To this end, we conducted a Systematic Literature Review (SLR) with seven research questions in which we identified and analyzed 118 studies dating from 2003 to 2022. The results indicate that the literature proposes many techniques for specific aspects (e.g., controlling cost/effort in SPL testing); however, other elements (e.g., regression testing and non-functional testing) still need to be covered by existing research. Furthermore, most approaches are evaluated by only one empirical method, most of which are academic evaluations. This may jeopardize the adoption of approaches in industry. The results of this study can help identify gaps in SPL testing since specific points of SPL Engineering still need to be addressed entirely.