Deep code search
Deep code search Gu et al., ICSE’18
The problem with searching for code is that the query, e.g. “read an object from xml,” doesn’t look very much like the source code snippets that are the intended results, e.g.:
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That’s why we have Stack Overflow! Stack Overflow can help with ‘how to’ style queries, but it can’t help with searches inside codebases you care about. For example, “where in this codebase are events queued on a thread?”
…an effective code search engine should be able to understand the semantic meanings of natural language queries and source code in order to improve the accuracy of code search.
DeepCS is just such a search engine for code, based on the CODEnn (Code-Description Embedding Neural Network) network model. During training, it takes code snippets (methods) and corresponding natural language descriptions (from the method comments) and learns a joint-embedding. I.e., it learns embeddings such that a method description and its corresponding code snippet are both mapped to a similar point in the same shared embedding space. Then given a natural language query, it can embed the query in vector space and look for nearby code snippets. Compared Continue reading

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