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University of Cyprus

The International Conference on Software and Systems Reuse, Product Lines, and Configuration (VARIABILITY 2026) invites high-quality contributions from researchers and practitioners in software engineering, systems engineering, and related disciplines focussing on a broad spectrum of methods, concepts, and tools for variability. VARIABILITY aims to be the premier forum for the exchange of ideas, experiences, and results in all aspects of software and systems variability management, reuse, software configuration, and customization. As software and systems become increasingly configurable, reusable, and adaptable, managing their variability across all lifecycle phases is more critical—and more challenging—than ever.

SEIT Lab, Department of Computer Science, University of Cyprus

The International Symposium on Software Reliability Engineering (ISSRE) is the leading conference on software reliability research and practice. ISSRE focuses on techniques and tools for assessing, predicting, and improving the reliability, safety, security, and resilience of software systems. As modern software increasingly integrates AI/ML components, operates autonomously, and spans cloud-to-edge environments, ensuring reliable system behavior is more critical than ever.

Tel.: [+35722-892693];     Email: george@ucy.ac.cy

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