Attention to aspect oriented programming (AOP) is rapidly growing as its benefits in large software system development and maintenance are increasingly recognized. However, existing large software systems, which could benefit most from refactoring into AOP, still remain unchanged in practice, due to the high cost of the refactoring. Automatic identification and extraction of aspects would not only enable migration of legacy systems to AOP, but also prevent current systems from accumulating scattered and duplicated code. In this paper, we present the design, implementation, and evaluation of an aspect mining analysis, which automatically identifies desirable candidates for refactoring into AOP, without requiring input from the user or predefined queries. By exploiting the program dependence graph and abstract syntax tree representations of a program, our analysis is able to automatically identify a much larger set of valuable refactoring candidates than current aspect mining techniques, as demonstrated by an empirical evaluation of our automatic mining analysis on two large software systems.