Code search techniques return relevant code fragments given a user query. They typically work in a passive mode: given a user query, a static list of code fragments sorted by the relevance scores decided by a code search technique is returned to the user. A user will go through the sorted list of returned code fragments from top to bottom. As the user checks each code fragment one by one, he or she will naturally form an opinion about the true relevance of the code fragment. In an active model, those opinions will be taken as feedbacks to the search engine for refining result lists. In this work, we incorporate users’ opinion on the results from a code search engine to refine result lists: as a user forms an opinion about one result, our technique takes this opinion as feedback and leverages it to re-order the results to make truly relevant results appear earlier in the list. The re- finement results can also be cached to potentially improve future code search tasks. We have built our active refinement technique on top of a state-of-the-art code search engine— Portfolio. Our technique improves Portfolio in terms of Normalized Discounted Cumulative Gain (NDCG) by more than 11.3%, from 0.738 to 0.821.