Call for Papers

We solicit high-quality contributions (research, practice, tools, and position papers) on the application of systems and software assurance principles and techniques to assure that the dependability properties of critical software-intensive systems have been met. ASSURE 2024 additionally solicits papers that contain new, forward-looking, ideas with emerging results and concrete plans for comprehensive empirical validation, works-in-progress, as well as reflections that examine current research under a new lens, calling for future research directions. Papers should attempt to address the workshop goals in general, which are to: 

• explore techniques for assurance of software-intensive systems (especially those enabling autonomy) such as assurance cases including structured argumentation, graphical notations, narrative forms, etc. 

• examine the role of systems assurance in the engineering lifecycle of critical systems; 

• identify the dimensions of effective practice for systems and software assurance; 

• investigate the relationship between dependability techniques and systems assurance; and, 

• identify critical research directions, define a roadmap for future development, and formulate challenge problems.

Topics of Interest

Topics of interest include but are not limited to: 

• Assurance and certification issues in emerging paradigms, e.g., adaptive and autonomous systems, systems integrating novel machine learning and artificial intelligence technologies, including deep learning, large language models (LLMs), etc., including self-driving cars, uncrewed and conventionally piloted aircraft systems, digital health and decision making systems, rail transport, etc.

• The intersection of systems assurance and traditional systems engineering disciplines such as requirements engineering and model-based engineering.

• Empirical assessment of systems assurance in different domains and certification regimes.

• Standards: Industry guidelines and standards that are normally used or required for systems assurance, e.g., ISO 26262, ISO/PAS 21448, IEC 61508, ARP 4754B, DO-178C, UL4600, etc., and their continued applicability to assurance of autonomous systems. 

• Dependable architectures for AI/ML: How do conventional fault-tolerant architectures and design measures such as diversity, independence, and partitioning relate to assurance of AI/ML systems and software? 

• Safety/reliability analysis techniques, performance requirements for ML-based systems, and assurance: specifically, how can techniques such as fault tree analysis, Markov models, and probabilistic modeling aid in defining ML performance requirements and in their assurance? 

• The role of formal methods, associated tools and techniques in serving as evidence for system and software assurance for their quality attributes of safety, security, maintainability and dependability in general.

• Domain-specific assurance issues, in domains such as aerospace, automotive, rail, healthcare, defense and power.

• The use and applicability of assurance cases to the above topics.