New Trends in Machine Learning, System and Digital Twins

Dear Colleagues,

This Special Issue, titled "New Trends in Machine Learning, System and Digital Twins", aims to provide a platform for researchers and practitioners to share the latest advancements and ideas at the intersection of machine learning, software and system engineering, and their applications. It endeavors to bridge the gap between these dynamic fields, fostering innovation and collaboration. By bringing together diverse perspectives, this Special Issue aims to drive the development of new techniques and methodologies that can enhance the efficiency, quality, and intelligence of complex software systems.

The scope encompasses a wide range of research areas related to the integration of machine learning and software engineering. It includes theoretical studies exploring fundamental concepts and principles underlying their combination, as well as practical applications demonstrating how machine learning can be effectively utilized in software development, maintenance, and evolution. This Special Issue also welcomes contributions on the challenges and opportunities presented by this convergence, along with discussions on ethical, legal, and social implications.

This Special Issue will focus on (but is not limited to) the following topics:

• Machine learning-assisted software development: Including the use of machine learning algorithms for tasks such as code generation and refactoring and software defect prediction.
• Software engineering for machine learning systems: Topics such as the design, development, and testing of machine learning models, including issues like model interpretability and fairness.
• Hybrid approaches: Combining traditional software engineering methods with machine learning techniques to create more intelligent and adaptable software systems.
• Machine learning-driven complex systems and applications: Including cutting-edge machine learning techniques applied to building complex systems and applications within but not limited to the domain of software, business, and financial engineering.
• Empowering human-centered innovation: Tackling real-world challenges through AI-driven solutions grounded in the cutting edge of design innovation and responsible AI frameworks to align technical capabilities with human needs and societal values.
• Embed context-aware AI systems: Leveraging generative AI and adaptive machine learning models within design methods, e.g., user journey mapping, prototype iteration, and usability testing, to co-create human-centered design solutions.
• Case studies and real-world applications: Practical examples of how the synergy between machine learning and software engineering has been successfully implemented in various domains, such as healthcare, finance, and transportation.

Dr. Yilong Yang
Dr. Bingqing Shen
Dr. Zichao Nie
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• machine learning
• software engineering
• automated software development
• software quality assurance
• software architecture
• model integration