  Architectural decay imposes real costs in terms of developer effort, system
correctness, and performance. Over time, those problems are likely to be
revealed as explicit implementation issues (defects, feature changes, etc.).
Recent empirical studies have demonstrated that there is a significant
correlation between architectural "smells" -- manifestations of architectural
decay -- and implementation issues. In this paper, we take a step further in
exploring this phenomenon. We analyze the available development data from 10
open-source software systems and show that information regarding current
architectural decay in these systems can be used to build models that
accurately predict future issue-proneness and change-proneness of the systems'
implementations. As a less intuitive result, we also show that, in cases where
historical data for a system is unavailable, such data from other, unrelated
systems can provide reasonably accurate issue- and change-proneness prediction
capabilities.
