@misc{14485,
  author = {Shaukat Ali},
  title = {Uncertainty-Wise Testing},
  abstract = {Uncertainty-Wise testing explicitly integrates known uncertainty about the behavior of the System Under Test (SUT), its operating environment, and interactions between them in test design, generation, optimization, and execution with the following two key objectives. First, to ensure that the SUT deals with known uncertainty appropriately. Second, to learn new uncertainties such that the SUT{\textquoteright}s implementation can be improved to guard against these uncertainties, when it is operational. The necessity to integrate uncertainty in testing is becoming imperative because of the emergence of new types of intelligent and communicating software-based systems, e.g., Cyber-Physical Systems (CPSs). Intrinsically, such systems are exposed to uncertainty because of their interactions with highly indeterminate physical environment including human. On top of that, these systems are becoming more and more autonomous, i.e., making decisions themselves at runtime (e.g., self-healing and self-configuration) and thus introduce an extra layer of complexity to test these systems. This keynote first focuses on novel challenges posed by uncertainty-wise testing of CPSs. Second, it presents some research results from the applications of novel approaches for uncertainty-wise testing of CPSs that were devised based on model-based testing, search-based testing, and machine learning techniques.},
  year = {2017},
  journal = {Advances in Model-Based Testing (A-MOST), Tokyo, Japan},
}