Graduation date: 2008Most of the work so far in the subfield of Gender HCI has followed a theory-driven approach. Established theories, however, do not take into account specific issues that arise in end-user debugging. We suspected that there may be important information that we were overlooking. We therefore employed a methodology change: turning to data mining techniques to find hidden patterns and relationships in females' and males' feature usage patterns. This thesis reports two data mining studies to help discover complex ties among static, dynamic, and success data collected in end-user debugging sessions. Study 1 was our first step, and was used to derive new hypotheses about females' and males' strategies and behaviors. In Study 2, we then applied different data mining algorithms to a larger data set to describe, summarize, segment, and detect interesting patterns. We found that most of the factors that tied with females' success in debugging were different than those that tied with males' success in debugging and vice versa. The results will ultimately help Gender HCI researchers better support end-user debuggers of both genders.