With the exponential expansion of information on the internet, users are increasingly encountering challenges in locating the necessary information within an intricate web information system (WIS). Meanwhile, developers struggle to craft user interfaces that deliver optimal user experiences (UX) within complex web architectures. Chatbots, emerging as integral components within new WISs, serve as complementary elements to traditional graphical user interfaces (GUIs). However, the absence of established methods providing clear guidelines or practices for implementing Chatbots on an existing WIS remains a notable gap. This study aims to address this gap by proposing an approach that transforms existing web functionalities into conversational interfaces (i.e., Chatbot interfaces). We present a comprehensive step-by-step guideline and a set of patterns to facilitate the conversion, referred to as “Chatbotification.” To validate the feasibility of our proposed approach, we implemented Chatbots11https://www.youtube.com/watch?v=Xbxnnt2GrfM using two distinct frameworks, Rasa and GPT. The conducted experimental results show that all participants also found that the Chatbots are easy to use and understandable, while it takes an average of a few interactions to complete a given task with the Chatbots.

The Software Engineering (SE) course primarily teaches students how to develop and maintain high-quality software using systematic processes, methods, and tools. Five teaching issues in the SE course, including course breadth, limited teaching assistant resources, limited teaching interaction, learning weaknesses, and poor team communication, hinder students from absorbing technical concepts, knowledge, and skills for necessary subjects in the SE field. In recent years, Digital Interactive Learning has received increasing attention. Among them, Chatbot was significantly under the spotlight. It can confirm the user's intention by analyzing the content of the dialogue and simulating a human conversation. To address the teaching issues for the SE course and leverage the benefits brought by Chatbots, in this study, we designed a Chatbot system, called TABot, to assist teaching and learning, especially for the SE course. This study designed multiple features that support students' learning, such as frequently asked questions, requests for course materials, and adaptive quiz practices. Furthermore, TABot also provides team supporting functions, such as reading the user requirements of their team project, group member contribution analysis, and GitHub commit retrieval. After being applied to an SE course, 80% of the students agreed that TABot could help them learn better for the SE course.

With the progressive development of technology, programming learners have significantly increased. However, the lack of human tutors and the rapidly updating information cause the learners to spend a considerable amount of time browsing and filtering authentic online resources, and decrease learning efficiency. Although many coding websites and programming communities can provide credible advice, it is still a challenge for learners to figure out their accurate questions. Therefore, we devised a Chatbot system, named PSAbot, to consider the above issue. PSAbot supports keyword extraction and analysis for multiple posts to guide the users through questions. PSAbot applies word embedding, sentence similarity, LDA (Latent Dirichlet Allocation) topic modeling, and weighting functions to help filter out redundant information and decrease the time cost of browsing, and further improve the learning efficiency. The conducted experiments show that about 80% of the Top1 answers recommended by PSAbot can largely meet the user expectations.

The microservice system architecture (MSA) outperforms the monolithic system architecture in terms of maintainability, extensibility, scalability, and fault tolerance. This is prompting a widescale migration of software systems from existing monolith systems to MSA. Most microservice systems utilize container technology for deployment. The fact that Kubernetes (K8s) provides a fully-fledged toolchain for managing container-based applications is prompting many organizations to adopt the K8s protocol for microservice system deployment and operations. Microservice monitoring is essential to the success of any service operation. The collection of logs and aggregation of metrics by most existing microservice monitoring systems is somewhat intrusive. Furthermore, the heterogeneity of Kubernetes technology means that most monitoring methods are inapplicable in situations where microservices are developed for a system using a variety of underlying languages and platforms. In the current study, we developed a monitoring mechanism that provides various metrics specific to microservice systems in a nonintrusive way. The proposed K8s-based microservice monitoring system, referred to as KMamiz (Kubernetes-based Microservice Analysis and Monitoring using Istio and Zipkin), enables the construction and visualization for service-level/endpoint-level dependency graphs and endpoint request chains, and the service cohesion/coupling analysis to enhance system quality for the development team.