Add paper.

Author: Miltos Allamanis

_publications/yao2019coacor.markdown

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+---
+layout: publication
+title: "CoaCor: Code Annotation for Code Retrieval with Reinforcement Learning"
+authors: Z Yao, JR Peddamail, H. Sun
+conference: 
+year: 2019
+bibkey: yao2019coacor
+---
+To accelerate software development, much research has been performed
+to help people understand and reuse the huge amount of available code
+resources. Two important tasks have been widely studied: code retrieval,
+which aims to retrieve code snippets relevant to a given natural language
+query from a code base, and code annotation, where the goal is to annotate a 
+code snippet with anatural language description. Despite their advancement in recent
+years, the two tasks are mostly explored separately. In this work, we
+investigate a novel perspective of Code annotation for Code retrieval 
+(hence called “CoaCor”), where a code annotation model is trained
+to generate a natural language annotation that can represent the
+semantic meaning of a given code snippet and can be leveraged by
+a code retrieval model to better distinguish relevant code snippets
+from others. To this end, we propose an effective framework based
+on reinforcement learning, which explicitly encourages the code
+annotation model to generate annotations that can be used for the
+retrieval task. Through extensive experiments, we show that code
+annotations generated by our framework are much more detailed
+and more useful for code retrieval, and they can further improve
+the performance of existing code retrieval models significantly.