Foundations of Reliability and Dependability • Principles, models, metrics, empirical methods, and theories of software reliability, resilience, robustness, and safety • Systematic approaches to fault prevention, fault removal, fault tolerance, and fault forecasting in modern software systems • Testing and debugging, formal methods, model checking, static/dynamic analysis, verification, and runtime assurance Reliability in AI-Driven and Autonomic Systems • Reliability engineering for AI-enabled, autonomous, self-adaptive, and cyber-physical systems • Assurance, testing, verification, and certification of AI/ML components, including foundation and generative models • Reliability of AI-generated code: validation, verification, explainability, defect analysis, and trustworthy automation of development tasks • Impact of AI on software lifecycle processes (design, testing, evolution, operations, and quality management) AI Techniques for Reliability Engineering • Machine learning for defect prediction, anomaly detection, debugging assistance, fault localization, and test automation • Learning-based approaches to self-healing, resilience management, predictive maintenance, and reliability optimization • Reliability governance in AI-driven DevOps pipelines, including transparency, interpretability, and auditability Software Reliability in Emerging System Domains • Reliability assurance for cloud, edge, IoT, 5G/6G, cyber-physical, high-performance, and network softwarization environments • Dependability of open-source ecosystems, data-driven pipelines, model hubs, and AI-assisted contributions • Benchmarking, stress testing, workload modeling, and measurement frameworks for large-scale and AI-based systems Trustworthiness, Security, and Responsible Software Engineering • Intersections of reliability with security, privacy, fairness, transparency, and regulatory compliance • Societal, ethical, and human impacts of pervasive AI-enabled software systems • Responsible governance of AI-based systems, including lifecycle assurance, auditability, and risk analysis Human-Centered, Empirical, and Reproducible Reliability Research • Field studies, experience reports, user studies, and human factors in reliability engineering • Public datasets, benchmark suites, reproducibility packages, and replication/negative- result studies • Tooling, automation, continuous reliability monitoring, observability, and operational feedback loops