Abstract
Researchers have identified several quality metrics to predict defects that rely on different types of information. However, these approaches lack metrics to estimate the effort of program understandability of system artifacts. Code that is understandable is often considered more maintainable. This paper briefly describes a dissertation which presents novel metrics to compute the cognitive complexity based on program slicing. These metrics help identify code that is more likely to have defects due to being challenging to comprehension. A thorough empirical investigation into how cognitive complexity correlates with and predicts defects is performed. Finally, this paper discusses potential direction for future research based upon the work conducted, as well as a reflection on the PhD study, providing advice for current students.