Track: Quality in Adaptive Software
Software systems are subject to various types of run-time uncertainties and variabilities. This is particularly true with regard to the emergence of machine learning-enabled systems where uncertainty is an inherent property of the system, giving rise to various design-time and run-time challenges. Over the years, adaptation has emerged as one of the solutions to tackle such changes during system execution. Adaptive software represents an established approach to coping with today’s systems complexity, uncertainty, and dynamicity. Adaptivity enables software systems to adapt their structure/behaviour during their execution in order to address changes in their environment to maintain and achieve their objectives. Adaptivity supports and enforces resilience, robustness, flexibility, autonomy, evolution, and sustainability, as well as other quality attributes.
Available solutions in various application domains propose various adaptation mechanisms for self-optimization, self-configuration, self-organization, self-protection, self-learning. In this track, we invite contributions addressing quality aspects in adaptive software. Quality aspects may concern software metrics or quality attributes to assess the efficiency of the adaptation mechanisms, trade-off analysis between the cost and the benefit of the adaptation mechanisms, the usability and re-usability of available adaptation mechanisms in other systems or application domains, the evolvability of adaptation mechanisms, the use of machine learning for proactive adaptation, just to name some. We invite researchers and practitioners to share their experience and research results in what is meaningful to consider as quality in adaptive software and how they assess this quality.
This track is open to a wide range of topics concerning quality in adaptive software, including, but not limited to:
Quality of the adaptive mechanisms;
Quality of the software achieved through adaptive mechanisms;
Quality of adaptive software at design time;
Quality at adaptive software runtime;
Quality metrics and standards specific to adaptive systems;
Trade-offs between development effort and qualities;
Evaluation mechanisms for adaptive software;
Use of AI/GenAI for enhancing the quality of adaptive software;
Case studies and best practices in developing high-quality adaptive software;
Challenges and solutions in testing and validating adaptive software;
User experience and usability aspects in adaptive software;
Tool support for the development and quality assessment of adaptive software.
Matteo Camilli, Politecnico di Milano, Italy
Mauro Caporuscio, Linnaeus University, Sweden
Roberto Casadei, Università di Bologna, Italy
Maria da Loura Casimiro, University of Lisbon, Portugal
Martina De Sanctis, Gran Sasso Science Institute, Italy
Sona Ghahremani, Hasso Plattner Institut for Digital Engineering, University of Potsdam, Germany
Elsy Kaddoum, IRIT, University of Toulouse, France
Christian Krupitzer, Universität Hohenheim, Germany
Mahyar Moghaddam, University of Southern Denmark, Denmark
Elisa Yumi Nakagawa, University of São Paulo, Brasil
Kenji Tei, Tokyo Institute of Technology, Japan
Claudia Raibulet is an Associate Professor at the Universita' degli Studi di Milano-Bicocca in Milan, Italy and an Assistant Professor at the Vrije Universiteit Amsterdam, in The Netherlands. She obtained her Ph.D. from Politecnico di Torino, Italy. Her main research interests concern software engineering topics, with a focus on the development of self-adaptive systems and the quality assessment in software systems. She is an editorial board member of the Information and Software Technology Journal and an associate editor of the IEEE Access Journal. She leaded two special issues on Software Architectures for Smart and Adaptive Systems. She is involved in referee activities for various international journals, as well as in organizing and program committees for international conferences and workshops. More than eighty research articles published in international journals, conferences, and workshops are co-authored by her.
Karthik Vaidhyanathan is an Assistant Professor at the Software Engineering Research Center and member of the leadership team, Smart City Research Center at the International Institute of Information Technology, Hyderabad, India. He obtained his Ph.D. from the Gran Sasso Science Institute, Italy and did his postdoc at the University of L’Aquila, Italy. His main research interests lie in the intersection of software architecture and machine learning with a specific focus on building self-adaptive software systems. As a part of his research activities, he serves as a reviewer/organizing committee member in various workshops, conferences, and journals. He is also an editorial board member of IEEE Software. Karthik also poses more than five years of industrial experience as an employee and as a consultant in developing and deploying machine learning products/services.