Component-based software development (CBSD) is an alternative approach to constructing software systems that offers numerous benefits, particularly in decreasing the complexity of system design. However, deploying components into a system is a challenging and error-prone task. Model-checking is one of the reliable methods to systematically analyze the correctness of a system. Its brute-force checking of the system's state space assists to significantly expand the level of confidence in the system. Nevertheless, model-checking is limited by a critical problem called state space explosion (SSE). To benefit from modelchecking, an appropriate method is required to reduce SSE. In the past two decades, a great number of SSE reduction methods have been proposed containing many similarities, dissimilarities, and unclear concepts in some cases. This research, firstly, plans to present a review of SSE handling methods and classify them based on their similarities, principle, and characteristics. Second, it investigates the methods for handling SSE problem in the verification process of CBSD and provides insight into the potential limitations, underlining the key challenges for future research efforts. INDEX TERMSComponent-based software development, Verification of software components, Modelchecking, State space explosion.

Abstract. The purpose of this study is twofold: 1) to develop a service-learning-based module training artificial intelligence (AI) subject (SLBM-TAIS), and 2) to evaluate the effect of SLBM-TAIS on pre-service teachers’ (PSTs’) practical knowledge and motivation, as well as primary school students' attitude towards AI in China. Participants of this study comprised 60 PSTs and 107 primary school students. The experimental research in this study followed the quasi-experimental non-randomized pre-test and post-test control group design. The PSTs were divided into experimental and control groups, and the primary school students followed the same grouping. The PSTs in the experimental group taught AI subjects to the primary school students in the experimental group, while the PSTs in the control group taught AI subjects to the primary school students in the control group. The results of the study showed that SLBM-TAIS was effective in training PSTs to teach AI subjects to primary school students. Furthermore, the SLBM-TAIS developed in this study offered a unique technique for training PSTs and primary school students that could increase PSTs' practical knowledge and motivation, as well as primary school students' attitudes toward AI. The findings from this study are important in the field of educational psychology, and its contribution has several theoretical and practical implications.   Keywords: Attitude; artificial intelligence; pre-service teachers; primary school students; practical knowledge; motivation

Summary Ontologies play a crucial role in multiagent systems (MASs) development, especially for domain knowledge modeling, interaction specifications, and behavioral aspect representation. Domain‐specific ontologies can be developed in an ad hoc or systematic manner through the incorporation of ontology development steps on the basis of agent‐oriented methodologies. Developing such ontologies, however, is challenging because of the extensive amounts of knowledge and experience required. Moreover, since many ontologies cater for very specific domains, the question arises of whether some can be reused for faster systems development. This paper attempts to answer this question by proposing an ontology pattern classification scheme to allow the reuse of existing ontology knowledge for MAS development. Specifically, ontology patterns relevant to the design problem at hand are identified through the pattern classification scheme. These patterns are then reused and shared among agent software communities during the system development phase. The effectiveness of the proposed approach is validated using a restaurant‐finder MAS case study. Our findings suggest that utilization of the classified ontology patterns reduces development time and complexity when dealing with domain‐specific applications. The scheme also seems useful for software practitioners, where searching and reusing the patterns can easily be done during the analysis, design, and implementation of MAS development.

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