Program debugging is a process of identifying and fixing bugs. Identifying root causes is the hardest, thus the most expensive, component of debugging. Developers often take a slice of the statements involved in a failure, hypothesize a set of potential causes in an ad hoc manner, and iteratively verify and refine their hypotheses until root causes are located. Obviously, this process can be quite tedious and time-consuming. Furthermore, as software systems are getting increasingly complex, the inefficiencies of the manual debugging process are getting magnified. Many automated approaches have been proposed to facilitate program debugging. All these approaches share the same ultimate goal, which is to help developers quickly and accurately pinpoint the root causes of failures. This course will cover state-of-the-art automated debugging approaches from both practical and research perspectives and will consist of two main parts. The goal of the first part is two folds: 1) To turn program debugging from a black art (as many believe) into a systematic and well-organized discipline; and 2) To provide students with enough background information to read and understand the scientific literature. The topics which will be covered in the first part are: How Failures Come To Be, Tracking Problems, Making Programs Fail, Reproducing Problems, Simplifying Problems, Scientific Debugging, Deducing Errors, and Mining and Detecting Anomalies. The second part of the course will survey the related literature by dividing it into four broad categories, namely static- analysis-based, dynamic-analysis-based, model-based, and empirical approaches.