• A dynamic trend analysis framework for Stack Exchange communities is introduced. • Two indicators for measuring topic growth are introduced. • A classifier to identify ChatGPT-related questions is introduced. • Tag clustering combining Inclusion Index with Affinity Propagation is used. • An overhauled visualization tool from our previous work is presented. Technological and methodological trends emerge at unprecedented rates, attracting developers to explore their potential and seek advice in online social networks. In this spectrum, ChatGPT has become a popular technology used for generating content to satisfy user queries while developers also integrate its mechanisms into their applications. Social networks usually revolve around technological trends through relevant announcements, posts, and questions. The primary goal is to demystify and evaluate the content surrounding questions from developers on Stack Overflow (SO) regarding a trending technology or method, in this case, ChatGPT. We present a topic-oriented trend analysis framework for analyzing questions from Stack Exchange communities, formulating a case study with five Research Questions (RQs) adapted to ChatGPT-related queries posted on Stack Overflow. The proposed framework contains different components aimed at extracting the main topics of relevant questions, providing analytics and pipelines for evaluating and comparing topic popularity, difficulty, and trending ability, as well as filtering irrelevant questions. The analysis uncovers diverse topics referring to technologies, platforms, and programming languages associated with ChatGPT usage, as well as a variety of purposes related to textual, audio, and image data. Additionally, the framework helped in identifying one popular and one unpopular topic, along with one difficult and four rising topics. In the context of ChatGPT, statistical tests indicated that Langchain is a more popular framework than Flutter and that questions related to the ChatGPT API concentrate lower scores but more answers than questions associated with LLMs. Overall, this paper demonstrates that the introduced framework can be utilized for studying multiple objectives covering a trending subject, as its mechanisms rely exclusively on the standard characteristics of Stack Exchange (SE) questions. Also, the methodologies and findings can offer insights and ideas for future research and experiments.

With medical technological advances being developed in a rapid pace, the need for effective Scientific Software Development (SSD), that can process, store and visualize medical data is ever growing. Particularly during the COVID-19 pandemic, the medical community came together to produce efficient solutions to tackle this global setback. Programmers and developers have an active role in the procurement of medical software, with many of them exchanging knowledge and opinions in Q&A portals like Stack Overflow (SO) about methodologies, techniques and programming queries. In this study we present a data-driven framework that collects, filters, stores and analyzes issues and questions for medical applications from SO, visualizing them in an intuitive manner. To highlight the functionalities of our framework, we present a case study with COVID-19 SSD related questions, providing insights and valuable information about the status of the domain.