Recently, there has been a significant increase in the number of Artificial Intelligence (AI) systems, software, and components. As a result, it is crucial to evaluate their quality. Quality models for AI have in fact been proposed, but there is a lack of Systematic Mapping Studies (SMS) for quality models in AI systems, software, and components. The goal of this paper is to understand, classify, and critically evaluate existing quality models for AI systems, software, and components. This study conducts an SMS to investigate quality models proposed by various authors in the past. The study only found quality models for AI systems and software. So far, the SMS has revealed no work on AI software component quality models. Finally, the limitations of the quality models and the implications for future research and development efforts are discussed.

Many organizations that have the global software development (GSD) projects use communication technologies to connect their virtual teams. However, the virtual team faces various challenges and issues in the process of the GSD. One of the significant challenges is obtaining an efficient communication among team members. This study focuses on the communication factors among the virtual teams highlighted in the literature. The communication factors are related to the temporal distances, geographical distances, socio-cultural, access to training, technological problems that hindering communications, the communication within the status of development process, personal communication skills and language differences. The objective of this study is to examine the communication factors and identify the related issues which are commonly occurring between the virtual teams in the global software development. A survey was conducted in different sectors. The findings indicate that some of important points related to the communication factors contribute to the success of the GSD.

In Component Based Software Development (CBSD), applications are built from existing components either by assembling or replacing software parts. Reusing components may lead to faster software development and subsequently reduce cost and provide higher product quality. In CBSD, software component models define what components are and how they compose. However, no research has been done to assess the quality of software component models, to assess the characteristics of software component design. This paper proposed a software component quality model specifically to answer the question what characteristics make good component. A Systematic Literature Review (SLR) has been conducted by defining a robust protocol that combines automatic searches from different sources. The finding of the SLR has contributed to the development of quality model for CBSD, i.e. a proposed component quality model with metrics which is specific to software component design.

The enthusiasm of IT entrepreneurs in producing Internet of Things (IoT) systems is undeniable as currently, the number of connected devices is enormously increasing. Many research has been done to efficiently develop IoT systems. IoT systems are usually engineered from scratch. IoT component models have been introduced but lack of generic development framework or model that supports high reusability and loose coupling in dealing with the heterogeneous devices that can hinder its development. Thus, an IoT component model is proposed. Meta-modelling has been used to define the component model where the specific interaction and composition standard in a component are abstracted. IoT component model is intended to develop a prototype for IoT development. With this IoT prototype, IoT system developers will not need to develop everything from scratch every time, as generic components can be reused even when it is applied in different domains or during system enhancement is required. Smart home IoT system has been selected as a case study to evaluate our prototype tool. In this study, we provide an alternative way to develop IoT software in component-based software engineering method. A prototype has also been developed to assist reusability and reduce coupling between modules.

Ensemble learning plays a crucial role in deep learning (DL) by improving classification accuracy through the aggregation of multiple classifiers. However, traditional methods, such as bagging, boosting, and stacking, face limitations when combining heterogeneous classifiers trained on unrelated tasks, owing to their reliance on closely aligned objectives. To address these challenges, we propose Adjusted Exponential Scaling (AES), a novel approach that combines diverse classifiers using probabilistic outputs, such as those generated by the Softmax function. AES eliminates the need to modify DL architectures or interfere with their training processes, thereby significantly reducing computational costs while ensuring compatibility with existing systems. We demonstrate AES scalability in simulations, successfully combining the outputs of up to 40 classifiers while maintaining probabilistic integrity. A formal verification using the Z3 Satisfiability Modulo Theories (SMT) solver confirmed its accuracy and reliability. AES is designed for simplicity and adaptability, making it particularly well suited for dynamic applications such as deepfake detection and medical diagnostics, where real-time combination is critical. By providing a scalable and computationally efficient solution, AES expands the applicability of ensemble learning and establishes a foundation for future extensions to sequential and binary tasks, further enhancing its versatility in machine learning challenges.