Effectively searching code for specific programming task from code base has become an important research field of software engineering. This paper presents a description reinforcement based code search (DERECS) approach. DERECS first builds a codedescription pair corpus, analyzes both code and its natural language description, and extracts features about method calls and code structure. DERECS reinforces the description of code based on the method calls and code structure features, reduces the gaps between code snippet and natural language query, and expands the search scope. Evaluation is conducted against real-world queries, and the results show DERECS is significantly better than SNIFF and Krugle.