Advanced Software Engineering in the Age of Artificial Intelligence: Best Practices, Challenges, and Opportunities

Dear Colleagues,

Software engineering is undergoing a period of transformation as Artificial Intelligence (AI) increasingly influences the software lifecycle, from requirements engineering to maintenance. This Special Issue explores the intersection of two distinct but mutually reinforcing domains: AI for Software Engineering (AI4SE), which investigates how AI models can automate and optimize engineering tasks, and Software Engineering for AI (SE4AI), which addresses the rigorous architectural and quality requirements of developing intelligent systems.

As software systems grow in complexity, the industry must address the implications of AI-driven development on technical debt, cost management, and long-term maintainability. This issue seeks to bridge the gap between emerging generative capabilities and established engineering principles such as modularity, observability, architectural principles, and robust testing frameworks. We invite researchers and practitioners to submit work that provides empirical evidence, methodological advancements, and critical analysis of how AI integration impacts the software engineering discipline. Topics are in three related categories, including

I. AI for Software Engineering (AI4SE)

This category focuses on the utilization of machine learning and autonomous systems to enhance traditional engineering processes.

• Intelligent Requirement Engineering: Methods for automated requirement extraction, conflict detection, and specification refinement.
• Automated Software Quality Assurance: Enhancing testing protocols, static analysis, and defect prediction for large-scale, distributed systems.
• Repository Mining and Analysis: Utilizing AI to extract insights from software repositories to improve maintenance, evolution, and technical debt management.
• AI-Assisted Development Processes: Methodologies for integrating generative models into the CI/CD pipeline while maintaining secure and reproducible build environments.

II. Software Engineering for AI (SE4AI)

This category focuses on the application of rigorous software engineering principles to the development of robust and trustworthy AI models.

• Architectures for Intelligent Systems: Design patterns and architectural standards for deploying AI models within distributed and cloud-native environments.
• Trustworthiness and Explainability (XAI): Methodologies for auditing AI systems to ensure transparency, accountability, and reliability in critical domains.
• Life-Cycle Management for Machine Learning: Applying DevOps and MLOps principles to manage the deployment, monitoring, and versioning of machine learning components.
• Validation of Autonomous Behavior: Engineering frameworks to assess the robustness, performance, and safety of autonomous agents or learning-based systems.

III. Ethics, Sustainability, and Governance

This category examines the broader societal and operational impacts of integrating AI into software development.

• Ethical Software Engineering: Assessing the social impact of AI-driven systems and implementing frameworks for bias mitigation and algorithmic fairness.
• Sustainability and Resource Efficiency: Metrics and measurement models to evaluate the environmental and computational costs of AI-intensive software development.
• Governance and Compliance: Strategies for aligning AI-integrated software engineering practices with emerging regulatory standards for data privacy and security.

Prof. Dr. Yan Liu
Guest Editor

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