ISSRE 2026 - 37th IEEE International Symposium on Software Reliability Engineering (ISSRE 2026): Call for Contributions

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