Advances in Software Quality Engineering for Complex Systems

The realm of software quality in software engineering is integral to the development of robust and efficient systems. This is particularly crucial in the context of machine learning and artificial intelligence, where the reliability and accuracy of algorithms dictate their usability and effectiveness. Machine learning relies heavily on data-driven approaches to automate predictive and decision-making processes. Visual analytics in AI leverages computational thinking—a problem-solving process that uses computer science techniques—to analyze and visualize complex data. This process is essential for deciphering trends and patterns in large datasets, a task often encountered in machine learning projects. Ethical considerations in AI involve addressing biases in machine learning algorithms, ensuring privacy and fairness, and making AI systems accessible and beneficial for a diverse range of users. Sustainability, on the other hand, relates to developing AI solutions that are environmentally conscious and economically viable over the long term.

This Research Topic aims to understand recent advancements in software engineering, particularly in machine learning, artificial intelligence, computer vision, visual analytics, and software quality. It also tackles the latest advances in computational thinking and its role in the computational education arena. Furthermore, the Topic seeks to explore the role of computer vision and visual analytics in pushing boundaries in software solutions; while also aiming to bridge the gap between technological progress and ethical responsibility, focusing on AI ethics and sustainability in developing future-proof software solutions. This holistic approach combines cutting-edge technologies with a conscientious consideration of their impact on society and the environment, guiding the future of software engineering towards innovation, ethics, and sustainability.

This article collection invites authors to explore the intersection between software quality and engineering with advanced technologies like machine learning, artificial intelligence (AI), computer vision, and visual analytics. It also takes into account the role of computational thinking at different educational stages. Submissions may highlight the applications of computer vision and visual analytics techniques in enhancing software functionality, user experience, and overall quality. Authors are encouraged to consider the broader implications of their work in the context of AI ethics and sustainability, including the mitigation of biases in AI, the development of environmentally sustainable AI solutions, and the long-term viability of AI technologies in a rapidly evolving digital landscape.

Submissions can range from original research, empirical studies, theoretical models, systematic literature reviews, surveys, case studies, and lessons learned papers to practical applications and case studies. All papers will undergo a thorough peer-review process to ensure high-quality and impactful research.