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