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

*** Call for Contributions ***
37th IEEE International Symposium on Software Reliability Engineering
(ISSRE 2026)
October 20-23, 2026, 5* St. Raphael Resort and Marina
Limassol, Cyprus
https://cyprusconferences.org/issre2026/
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.
Topics of Interest
ISSRE 2026 invites high-quality contributions that advance the theory and practice of
software reliability across contemporary software-intensive systems, including systems
that incorporate AI/ML components. Topics of interest include, but are not limited to:
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
Research Track Paper Categories