Machine Learning and Software Intensive Systems: Theory, Methods and Applications
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: closed (10 March 2022) | Viewed by 4909
Special Issue Editor
Interests: artificial intelligence; machine learning; autonomous systems; autonomous shipping
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
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
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- machine learning for software engineering
- software development
- software verification and validation
- testing machine learning
- adversarial testing
- self-adaptive systems
- autonomous systems
- trustworthy machine learning
