Call for Papers

The increasing usage of machine learning (ML) coupled with the software architectural challenges of the modern era has given rise to two broad research challenges: i) Software architecture (SA) for ML-based systems and ii) ML techniques for (better) architecting any software system. In recent times, both the research and practitioner community have started exploring these research areas at the intersection of SA and ML. As a result, there have been emerging contributions from the scientific and practitioner community in these two research areas. However, these contributions are scattered across different communities of software engineering, self-adaptation, ML, etc. The goal of SAML 2021 is to bring together practitioners and the research community in one common platform to explore: i) how to come up with better SA practices for architecting ML-based systems; ii) how to leverage ML techniques to better architect software systems; iii) state of research and practice in architecting ML-based systems and in using ML techniques for architecting modern software systems. Further, SAML 2021 shall also provide a common forum to bring together both practitioners and researchers of SA and ML communities to identify and fill the research gaps that can benefit both communities. SAML 2021 seeks contributions in the form of full research papers, industry experience reports and short papers in topics including but not limited to:

• Architecture design, analysis and evaluation of ML-based systems
• Architecture frameworks, patterns and models for ML-based systems
• Integration of ML development process and software development processes
• Use of ML/AI for Architecting practices
• Architecting Self-adaptive systems using ML/AI
• Role of software architect in architecting ML-based systems
• Software Architecture and MLOps practices
• Quality conformance of ML-based systems
• Architecture and Technical Debt in ML-based systems
• Maintenance and Managing evolution of/in ML-based systems
• Case studies and experience reports
• Social and Organizational aspects of architecting ML-based software systems
• Architecting ML/data pipelines