Conference Information

ISSRE 2026: International Symposium on Software Reliability Engineering

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Submission Date:
2026-04-10
Notification Date:
2026-07-08
Conference Date:
2026-10-20
Location:
Limassol, Cyprus
Years:
37
CCF: B   ICORE: A   QUALIS: A2   Viewed: 71946   Tracked: 80   Attend: 15

Conference Partner Index (CP-I)

84.7 / 100
Ranked #129 of 5,647 conferences · Top 3%

#16 of 245 in Software Engineering

Academic recognition (35%)
92
Submission selectivity (20%)
81
Editions held (20%)
98
Community attention (10%)
70
Public record completeness (15%)
65

Inputs used: Listed as CCF B, ICORE A, QUALIS A2 · Acceptance rate: 27.6% (mean of 5 editions on file) · Editions on record: 37 · Researchers following it here: 80 · Researchers who opened this page in the past 24 months: 37

Missing from the public record: Past editions (+3.0) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 100% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-09-02

Call For Papers

ISSRE 2026 (International Symposium on Software Reliability Engineering) is a CCF B / ICORE A / QUALIS A2 conference held in Limassol, Cyprus on 2026-10-20. The paper submission deadline is 2026-04-10. Acceptance notifications are sent on 2026-07-08.

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
Last updated by Dou Sun on

Acceptance Ratio

Average acceptance rate: 33% over 14 years (1996–2012).

YearSubmittedAcceptedAccepted(%)
20121253830.4%
20111062725.5%
20101304232.3%
2009842125%
20081162925%
2007782633.3%
20061023837.3%
2005983232.7%
20041203932.5%
20032004120.5%
2002733345.2%
20011003838%
1998873944.8%
19961034139.8%

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