SEAMS 2027 (International Symposium on Software Engineering for Adaptive and Self-Managing Systems) is a ICORE A / QUALIS B3 conference held in Dublin, Ireland on 2027-04-26. The paper submission deadline is 2026-10-21. Acceptance notifications are sent on 2026-12-03.
We invite submissions of technical research papers describing original and unpublished results on software engineering for self-adaptive and self-managing systems, across the broad spectrum of topics of interest listed below. SEAMS 2027 will use a single-round submission system for the Research Track, with the possibility of submitting a revised version. Accepted papers will be published in the IEEE and ACM digital libraries. In addition, authors of distinguished papers will be invited to submit revised and extended versions of their work to a dedicated Special Issue of ACM Transactions on Autonomous and Adaptive Systems (TAAS).
Background
SEAMS is an ICORE-A ranked conference that applies software engineering methods, techniques, processes, and tools to support the construction of self-adaptive, self-managing, and autonomous systems that provide self-* properties such as self-configuration, self-healing, self-optimization, and self-protection.
SEAMS brings together researchers and practitioners from academia and industry to investigate, discuss, and advance the fundamental principles, state-of-the-art methods, and practical solutions for engineering self-adaptive and self-managing systems. The conference addresses both long-standing challenges, such as uncertainty, assurance, runtime decision-making, and feedback-loop engineering, and emerging challenges raised by AI-enabled software, foundation models, autonomous agents, adaptive cyber-physical systems, and regulation-aware deployment.
Topics of Interest
We welcome research contributions to all topics related to engineering self-adaptive and self-managing systems, including but not limited to:
Foundational concepts for self-adaptive and self-managing systems
Self-* properties, including self-configuration, self-healing, self-optimization, and self-protection
Uncertainty, partial knowledge, and runtime decision-making for software-intensive systems
Runtime models, variability, and feedback-loop engineering
MAPE-K and novel adaptation architectures
Mixed-initiative systems, human-in-the-loop, human-on-the-loop, and human-AI teaming
Socio-technical, ethical, legal, and governance challenges
AI, machine learning, and foundation-model-enabled self-adaptation
LLM-based, agentic, and multi-agent self-adaptive systems
Adaptive prompt engineering and retrieval-augmented adaptation
Automatic synthesis techniques
Control theory and reinforcement learning for adaptation
Simulation, digital twins, and predictive analysis for proactive runtime adaptation
Human-centered software development and explainability
Requirements engineering for adaptive and autonomous systems
Security, privacy, cyber-resilience, and adversary-aware adaptation
Architecture, design, and coordination of adaptive systems
Testing, verification, validation, certification, and runtime assurance
Adaptive safety cases and continuous assurance
Evolution, reuse, maintenance, and self-evolution
Processes, methodologies, and DevOps for self-adaptive systems
Self-adaptation for software engineering, including adaptive development tools and self-healing software pipelines
Formal methods for self-* systems
Domain-specific languages and programming language support
Sustainability-aware, energy-aware, carbon-aware, and resource-aware adaptation
Application areas and domains include but are not limited to Internet of Things, cyber-physical systems, cloud/fog/edge/mobile computing, edge-cloud continuum systems, bioengineering, quantum computing, robotics, smart environments, smart user interfaces, augmented/mixed reality, web/service-based applications, automotive systems, autonomous vehicles, smart cities, and critical infrastructure.
We welcome contributions from both academic and industrial perspectives, including papers that advance foundational methods, report practical experiences, present lessons learned from real-world deployments, or identify open challenges arising from industrial practice.
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