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We approach the important challenge of code autocompletion as an open-domaintask, in which a sequence-to-sequence code generator model is enhanced with theability to attend to reference code snippets supplied by a semantic code searchengine. In this work, we present a novel framework to precisely retrievetemplate functions as well as intent-snippet pairs and effectively train such aretrieval-guided code generator. To demonstrate the effectiveness of our modeldesigns, we perform extensive experiments with CodeSearchNet which containstemplate functions and CoNaLa which contains Stack Overflow intent-snippetpairs. We also investigate different retrieval models, including Elasticsearch,DPR, and our fusion representation search model, which currently holds thenumber one spot on the CodeSearchNet leaderboard. We observe improvements byleveraging multiple database elements and further gain from retrieving diversedata points by using Maximal Marginal Relevance. Overall, we see a 4%improvement to cross-entropy loss, a 15% improvement to edit distance, and a44% improvement to BLEU score when retrieving template functions. We seesubtler improvements of 2%, 11%, and 6% respectively when retrieving StackOverflow intent-snippet pairs. We also create a novel Stack Overflow-FunctionAlignment dataset, which consists of 150K tuples of functions and StackOverflow intent-snippet pairs that are of help in writing the associatedfunction, of which 1.7K are manually curated.

Generating Code with the Help of Retrieved Template Functions and Stack Overflow Answers