Uncertainty wise software engineering
Uncertainty is inherent in large-scale systems such as Cyber-physical systems, IoTs, and any other smart systems. In the last few years, explicitly considering uncertainty during the software/system development lifecycle has been recognized by the research community and also industry. Unfortunately, there lack suitable methodologies and tools to deal with uncertainty in an explicit manner. We are interested in a variety of topics, each of which aims to take a particular aspect of software/system development lifecycle (e.g., requirements, analysis, design or testing) and proposes innovative and practically-useful solutions that consider uncertainty as the first-class element. Such a solution will benefit from advanced technologies such as search algorithms, machine learning techniques. The thesis will also include empirical studies with real-world case studies.
- Uncertainty modeling
- Machine Learning (e.g., active and passive learning)
- Search Algorithms (e.g., Genetic Algorithms)
- Requirements engineering, model analysis, design, or testing
- Shaukat Ali
- Tao Yue