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