My own research centers on software engineering and performance evaluation. In both cases the emphasis is on experimental methodology, in an attempt to ground the results in empirical findings rather than unbased assumptions. This is part of a wider interest in experimental computer science in general.

I'm interested in why programs are difficult to understand. This includes looking into complexity metrics and how various code attributes may affect perceived complexity. Specific achievements include
Studies on variable names, including one that suggests a model of name formation, and another that demonstrated that misleading names are not uncommon and can be worse than meaningless names such as consecutive letters of the alphabet.
Development of a gamified platform for experiments on comprehension, and using it to show, inter alia, that loops counting down are slightly harder to comprehend than equivalent loops counting up.
Identifying code regularity as an attribute that affects code complexity. This is the first experimental demonstration of how the complexity of certain code depends on its context, implying that metrics such as MCC that just count features are overly simplistic.