Trends and Prospects in Software Engineering

Software engineering is at a turning point. Over the last few decades, it has provided the conceptual foundations, methods, and tools that underpin today’s digital society. However, the rapid emergence of artificial intelligence (AI) and data-driven technologies—together with other disruptive paradigms such as quantum computing, cyber–physical systems, and large-scale autonomous platforms—is reshaping how software is conceived, engineered, deployed, and maintained. Generative AI, large language models, quantum algorithms, and pervasive analytics challenge traditional development lifecycles, roles, competencies, and even the boundaries of the discipline itself.

We invite contributions that address the most critical trends shaping software engineering’s future—particularly the impact of AI and disruptive technologies (e.g., quantum computing), engineering of AI-intensive and hybrid systems, emergence of new professional profiles, and the redefinition of core principles in light of socio-technical, ethical, and sustainability concerns. This Special Issue in Applied Sciences seeks submissions that critically analyze these topics, propose novel methods and tools, report empirical evidence from industry and open-source contexts, or offer forward-looking perspectives on the evolving landscape of software engineering.

We welcome original research articles, review papers, case studies, and vision or position papers with a solid analytical or empirical basis. Interdisciplinary contributions bridging software engineering with AI, data science, quantum technologies, human–computer interaction, education, and organizational studies are especially encouraged.

Topics of interest include, but are not limited to, the following:

• Requirements and Early Lifecycle
  • Requirements engineering for AI-intensive, quantum, and other disruptive software systems;
  • Variability management and software product lines for complex, adaptive ecosystems;
  • Elicitation, negotiation, and evolution of requirements in human–AI and human–technology collaborative workflows.
• Design and Architecture
  • Architectural styles and patterns for AI-based, data-driven, quantum, and autonomous systems;
  • Design for sustainability, evolvability, and technical debt management in long-lived software;
  • Architecture-centric approaches to integrating AI components, quantum services, and other emerging technologies.
• Construction, Testing, and DevOps/MLOps
  • AI-augmented software construction (e.g., code generation, refactoring assistants, intelligent IDEs), including support for quantum and other disruptive platforms;
  • Automated and AI-driven testing, including testing of ML components, quantum software, and data pipelines;
  • Continuous integration, delivery, MLOps, and related practices for AI-intensive and hybrid (classical–quantum or heterogeneous) software systems.
• Maintenance, Evolution and Configuration Management
  • Software evolution and maintenance in AI-enabled, quantum, and data-intensive contexts;
  • Managing variability, configuration, and release engineering in large-scale ecosystems;
  • Legacy modernization and long-term evolution of software and models with AI and other disruptive technologies.
• Process, Management and Economics
  • Software life cycle processes in the age of AI, quantum, and analytics-driven process improvement;
  • Project, team, and portfolio management for AI-intensive, quantum, and multi-technology software development;
  • Economic and value-based analyses of adopting AI, quantum, and other disruptive tools, platforms, and practices.
• Quality, Professional Practice, Education and Future Directions
  • Quality models for AI-based, quantum, and data-intensive systems (robustness, explainability, trustworthiness, sustainability);
  • Ethical, legal, and societal implications of AI, quantum, and other disruptive technologies in software engineering practice;
  • Education and training for software engineers in AI-intensive, quantum, and human–technology hybrid environments;
  • Vision, empirical and theoretical studies on the future scope and social role of software engineering in the presence of disruptive technologies.

We invite your contributions and shared reflections to help define the next generation of software engineering.

Dr. Samuel Sepulveda
Prof. Dr. César Jesús Pardo
Dr. Enrique Moguel
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 250 words) can be sent to the Editorial Office for assessment.

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. Applied Sciences 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.