Retrieval on source code: a neural code search
Searching over large code corpora can be a powerful productivity tool for both beginner and experienced developers because it helps them quickly find examples of code related to their intent. Code search becomes even more attractive if developers could express their intent in natural language, similar to the interaction that Stack Overflow supports.
In this paper, we investigate the use of natural language processing and information retrieval techniques to carry out natural language search directly over source code, i.e. without having a curated Q&A forum such as Stack Overflow at hand. Our preliminary experiments using a benchmark derived from Stack Overflow and GitHub repositories shows promising results.
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|Obfuscation Resilient Search through Executable Classification|
|Retrieval on source code: a neural code search|