ABOUT

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.

TOPICS

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.

TRACK COMMITTEE

Chairs:  Claudia Raibulet (Universita' degli Studi di Milano-Bicocca , Italy) and  Karthik Vaidhyanathan (International Institute of Information Technology, Hyderabad, India)

Program Committee: 

• Christian Krupitzer, Universität Hohenheim, Germany

• Elisa Yumi Nakagawa, University of São Paulo, Brasil

• Elsy Kaddoum, IRIT, University of Toulouse, France

• Kenji Tei, Tokyo Institute of Technology, Japan

• Maria da Loura Casimiro, University of Lisbon, Portugal

• Martina De Sanctis, Gran Sasso Science Institute, Italy

• Matteo Camilli, Politecnico di Milano, Italy

• Mauro  Caporuscio, Linnaeus University, Sweden   

• Mahyar Moghaddam, University of Southern Denmark, Denmark

• Roberto Casadei, Università di Bologna, Italy

• Sona Ghahremani, Hasso Plattner Institut for Digital Engineering, University of Potsdam, Germany