The decision which hardware platform to use for a certain application is an important problem in computer architecture. This paper reports on a study where a data-mining approach is used for this decision. It relies purely on source-code characteristics, to avoid potentially expensive program executions. One challenge in this context is that one cannot infer how often functions that are part of the application are typically executed. The main insight of this study is twofold: (a) Source code characteristics are sufficient nevertheless. (b) Linking individual functions with the runtime behaviour of the program as a whole yields good predictions. In other words, while individual data objects from the training set may be quite inaccurate, the resulting model is not.