Complex information environments are often organized as hierarchies. However, computational models of Information Foraging Theory (IFT) have almost entirely ignored this fact. Models and tools for predicting programmer navigations have ignored people’s foraging behavior across hierarchies —called hierarchical foraging. Without modeling hierarchical foraging, our ability to build tools to support foraging through complex information environments, is restricted. To this end, in this thesis, we introduce a new predictive model, PFIS-HCL, that builds upon PFIS-V, the most recent IFT-based computational model, which models people’s foraging behavior among variants. With PFIS-HCL, we aim to fill the current gap in modeling programmers’ foraging behavior in variations situations. PFIS-HCL is the first predictive model that explicitly accounts for hierarchical foraging. It also accounts for changelogs as a patch-type that provides general overview of hierarchies. We empirically evaluated PFIS-HCL using the data from a prior user study and our results show that it achieves a greater predictive accuracy than previous computational models that overlooked hierarchical foraging.