  When implementing unfamiliar programming tasks, developers commonly search
code examples and learn usage patterns of APIs from the code examples or reuse
them by copy-pasting and modifying. For providing high-quality code examples,
previous studies present several methods to recommend code snippets mainly
based on information retrieval. In this paper, to provide better recommendation
results, we propose ROSF, Recommending cOde Snippets with multi-aspect
Features, a novel method combining both information retrieval and supervised
learning. In our method, we recommend Top-Kcode snippets for a givenfree-form
query based on two stages, i.e., coarse-grained searching and fine-grained
re-ranking. First, we generate a code snippet candidate set by searching a code
snippet corpus using an information retrieval method. Second, we predict
probability values of the code snippets for different relevance scores in the
candidate set by the learned prediction model from a training set, re-rank
these candidate code snippets according to the probability values, and
recommend the final results to developers. We conduct several experiments to
evaluate our method in a large-scale corpus containing 921,713 real-world code
snippets. The results show that ROSF is an effective method for code snippets
recommendation and outperforms the-state-of-the-art methods by 20%-41% in
Precision and 13%-33% in NDCG
