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<div>Code smells are structures in code that indicate the presence of maintainability issues. A significant problem with code smells is their ambiguity. They are challenging to define, and software engineers have a different understanding of what a code smell is and which code suffers from code smells.</div><div>A solution to this problem could be an AI digital assistant that understands code smells and can detect (and perhaps resolve) them. However, it is challenging to develop such an assistant as there are few usable datasets of code smells on which to train and evaluate it. Furthermore, the existing datasets suffer from issues that mostly arise from an unsystematic approach used for their construction.</div><div>Through this work, we address this issue by developing a procedure for the systematic manual annotation of code smells. We use this procedure to build a dataset of code smells. During this process, we refine the procedure and identify recommendations and pitfalls for its use. The primary contribution is the proposed annotation model and procedure and the annotators’ experience report. The dataset and supporting tool are secondary contributions of our study. Notably, our dataset includes open-source projects written in the C# programming language, while almost all manually annotated datasets contain projects written in Java.</div>

The coronavirus disease of 2019 (COVID-19) pandemic has severely crippled our globalized society. Despite the chaos, much of our civilization continued to function, thanks to contemporary information and communication technologies. In education, this situation required instructors and students to abandon the traditional face-to-face lectures and move to a fully online learning environment. Such a transition is challenging, both for the teacher tasked with creating digital educational content, and the student who needs to study in a new and isolated working environment. As educators, we have experienced these challenges when migrating our university courses to an online environment. Through this paper, we look to assist educators with building and running an online course. Before we needed to transition online, we researched and followed the best practices to establish various digital educational elements in our online classroom. We present these elements, along with guidance regarding their development and use. Next, we designed an empirical study consisting of two surveys, focus group discussions, and observations to understand the factors that influenced students' engagement with our online classroom. We used the same study to evaluate students' perceptions regarding our digital educational elements. We report the findings and define a set of recommendations from these results to help educators motivate their students and develop engaging digital educational content. Although our research is motivated by the pandemic, our findings and contributions are useful to all educators looking to establish some form of online learning. This includes developers of massive open online courses and teachers promoting blended learning in their classrooms.

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