Beihang University,
China
Tao Yue is a full professor at Beihang University. Her main research interests include model-driven engineering, uncertainty-aware software engineering, and quantum software engineering. She has published more than 180 high-quality research papers in the field of software engineering. She is currently the EiC of the Continuous Special Section on Quantum Software Engineering at the ACM Transactions on Software Engineering and Methodology (TOSEM), and also an Associate EiC of IEEE Transactions on Software Engineering (TSE). She is also on the editorial board of Empirical Software Engineering (EMSE), and Software and System Engineering (SoSyM). She has served as the program committee co-chairs of international conferences such as MODELS, EASE, SSBSE, and regularly serves as program committee members of ICSE, FSE, ASE, etc. She also initiated and co-chaired the development of the international standard of Precise Semantics for Uncertainty Modeling (PSUM).
Quantum and quantum-inspired optimization methods are increasingly explored as promising approaches for addressing complex combinatorial problems. Many software engineering (SE) tasks such as test case prioritization, requirements selection, release planning, and system configuration can be formulated as optimization problems, making them suitable candidates for these techniques. This lecture introduces several representative quantum and quantum-inspired optimization algorithms and focuses on how they can be applied to software engineering problems. In particular, the talk discusses how SE problems can be systematically encoded into optimization formulations, such as QUBO or Ising models, enabling them to be solved by quantum or quantum-inspired optimization methods. The lecture also reviews empirical studies that evaluate the effectiveness of these approaches in SE contexts. Through representative case studies, we examine how different encoding strategies influence solution quality, scalability, and computational performance when compared with classical optimization techniques.
