Machine Learning and Software Intensive Systems: Theory, Methods and Applications

Dear Colleagues,

Software-intensive systems (SIS) are increasingly adopting machine learning (ML) to enable adaptive and self-managing capabilities. Examples range from intelligent cyberphysical systems to robotics, smart homes, and autonomous vehicles. Machine learning enables these systems to learn and adapt to uncertain changes in their environment at run time and protect themselves from failures and adversaries. Development of novel methods and techniques for enabling adaptive and self-managing properties of software-intensive systems has been an active research field in recent years, with many promising advances. Deep learning, for example, has shown prominent performance in robotics and autonomous vehicle technologies, in tasks such as sensing and learning, and interaction with humans. Still, there remain many open challenges, including trustworthiness, that need to be addressed to realize the true potential of ML for SIS.

This Special Issue calls for innovative contributions in the theory, methods, and application of machine learning for software-intensive systems.

Topics of interest include but are not limited to the following:

• Challenges of developing, testing, deploying, and maintaining ML-driven SIS;
• Quality requirements for ML-driven SIS;
• Methods and techniques for improving the development process of ML-driven SIS (including ML-based methods and techniques);
• Verification and validation techniques for ML-driven SIS;
• Data(sets) for training ML-based SIS;
• Experiences from developing, testing, deploying, and maintaining ML-driven SIS;
• Risks associated with ML-based SIS;
• Trustworthy ML.

Dr. Dusica Marijan
Guest Editor

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