API Code Recommendation using Statistical Learning from Fine-Grained Changes Anh Tuan Nguyen , Michael Hilton , Mihai Codoban , Hoan Anh Nguyen , 4 2 1 2 Lily Mast , Eli Rademacher , Tien N. Nguyen , Danny Dig 1 Department 1 2 3 1 of Electrical and Computer Engineering, Iowa State University, USA 2 School of EECS, Oregon State University, USA 3 Microsoft, USA 4 College of Engineering and Computer Science, University of Evansville, USA developer learning an API (or trying to remember it) can waste a lot of time combing through a long list of API method names available on a receiver object. For example, invoking the code completion on an object of type String in JDK 8 populates a list of 67 possible methods (and 10 additional methods inherited from superclasses). The state-of-the-art research in code completion takes advantage of API usage patterns [7, 12, 36, 44], which researchers mine via the deterministic algorithms such as frequent itemset mining, pair associations, frequent subsequence or subgraph mining. When a recommendation is requested, these approaches analyze the surrounding context. If the context matches a previously identified pattern, the recommender will suggest the rest of the API elements
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