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Trends and Prospects in Software Engineering

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 31 January 2027 | Viewed by 1292

Editors


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Guest Editor
Departamento de Ciencias de la Computación e Informática, Universidad de La Frontera, Temuco 4811230, Chile
Interests: software engineering; software modeling; requirements engineering; variability modeling; feature modeling; software product lines; quantum software engineering

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Guest Editor
GTI Research Group, Departemento de Sistemas, Facultad de Ingeniería Electrónica y Telecomunicaciones, Universidad del Cauca, Popayán 190003, Colombia
Interests: SPI; hybrid software development models; agile approaches; agile software development

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Guest Editor
Department of Computer and Telematic System Engineering, University of Extremadura, Av. De la Universidad s/n., 10004 Cáceres, Spain
Interests: software engineering; quantum computing; smart systems; UAVs
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

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-anonymized 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.

Keywords

  • software engineering
  • artificial intelligence (AI)
  • AI-intensive software systems
  • quantum software engineering
  • software architecture for intelligent systems
  • software product lines and variability management
  • DevOps and MLOps
  • software quality and trustworthiness
  • software engineering education and professional practice
  • disruptive software technologies and future trends

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Published Papers (2 papers)

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Research

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31 pages, 2111 KB  
Article
Collaborative Support for Software Product Line Modeling and Project Management: A Generative AI-Enhanced Approach with Real-Time Synchronization
by Oscar Aguayo, Juan Lagos-Obando, Samuel Sepúlveda and Raúl Mazo
Appl. Sci. 2026, 16(17), 8466; https://doi.org/10.3390/app16178466 - 25 Aug 2026
Abstract
Background: Software Product Line (SPL) engineering relies on coordinated work over variability-intensive artifacts, yet existing SPL tools provide limited support for synchronized multi-user modeling and project-level collaboration. Objective: This paper presents a collaborative extension of VariaMos that combines synchronization based on Conflict-free Replicated [...] Read more.
Background: Software Product Line (SPL) engineering relies on coordinated work over variability-intensive artifacts, yet existing SPL tools provide limited support for synchronized multi-user modeling and project-level collaboration. Objective: This paper presents a collaborative extension of VariaMos that combines synchronization based on Conflict-free Replicated Data Types (CRDTs), awareness and governance mechanisms, and integrated artificial intelligence (AI)-assisted model authoring. Method: Following Wieringa’s Design Science methodology, the treatment was designed, implemented, and evaluated through unit and functional tests, collaborative proof-of-concept scenarios, controlled load experiments, an exploratory expert survey with 20 specialists, and a bounded preliminary evaluation of the AI-assisted component. Results: The environment supports project- and model-level synchronization, presence awareness, role-sensitive collaboration, comments, revision history, rollback, and vote-assisted conflict resolution. Load tests characterized the operational limits of the Yjs-based architecture and showed that partitioning users across model-specific collaborative spaces improves stability. The survey identified conflict handling, traceability, versioning, authorship, and review workflows as priorities; because it used a purposive non-probability sample, it does not support population-level or productivity claims. In a 100-case evaluation comprising 50 base prompts and 50 metamorphic follow-up prompts, a context-enhanced chatbot configuration reduced structural hallucination from 84% to 18% and increased reproducibility from 22% to 72%. These results are preliminary and limited to one LLM accessed through OpenRouter, one controlled prompt bank, and single-pass executions. Conclusions: The study demonstrates the controlled feasibility of combining multi-artifact SPL collaboration with integrated AI-assisted authoring, while identifying trade-offs among convergence, semantic control, recoverability, and latency. Full article
(This article belongs to the Special Issue Trends and Prospects in Software Engineering)

Other

Jump to: Research

27 pages, 458 KB  
Systematic Review
Automatic Fault Detection and Diagnosis in ROS-Based Robotic Systems Using Generative AI: A Systematic Literature Review
by Marta Cardoso, Rafael Arrais and Armando Sousa
Appl. Sci. 2026, 16(11), 5545; https://doi.org/10.3390/app16115545 - 2 Jun 2026
Viewed by 645
Abstract
The increasing complexity and distributed nature of Robot Operating System (ROS)-based robotic systems require advanced Fault Detection and Diagnosis (FDD) approaches that operate autonomously with minimal human intervention. The goal of this systematic literature review is to investigate how observability-driven FDD can be [...] Read more.
The increasing complexity and distributed nature of Robot Operating System (ROS)-based robotic systems require advanced Fault Detection and Diagnosis (FDD) approaches that operate autonomously with minimal human intervention. The goal of this systematic literature review is to investigate how observability-driven FDD can be automated in ROS-based robotic systems to minimise human effort. Through this lens, the review surfaces four recurring gaps that collectively limit observability-driven automation: rich telemetry sources—logs, traces, and metrics—exist in isolation and are rarely integrated into real-time detection pipelines or leveraged collectively to improve failure diagnostics; online monitoring enables automatic fault detection but depends heavily on predefined rules and expert configuration and interpretation; failure explanations are generated post hoc and rely heavily on logs; and systems remain largely reactive, lacking the continuous monitoring infrastructure needed to anticipate faults before they propagate. Although Large Language Models (LLMs) show considerable promise for automated fault explanation and natural language interaction with robotic systems, current implementations fall short of comprehensive, real-time monitoring that unifies logs, traces, metrics, and sensor streams with Artificial Intelligence (AI) reasoning. To address these gaps, this paper motivates hybrid architectures that combine observability-first design, runtime monitoring, static analysis, and agentic LLM-based reasoning, laying the groundwork for more proactive and autonomous fault management in ROS-based systems. Full article
(This article belongs to the Special Issue Trends and Prospects in Software Engineering)
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