OCoR
Qihao Zhu, Zeyu Sun, Xiran Liang, Yingfei Xiong, Lu Zhang
Abstract
Code retrieval helps developers reuse code snippets in the open-source projects. Given a natural language description, code retrieval aims to search for the most relevant code relevant among a set of code snippets. Existing state-of-the-art approaches apply neural networks to code retrieval. However, these approaches still fail to capture an important feature: overlaps. The overlaps between different names used by different people indicate that two different names may be potentially related (e.g., "message" and "msg"), and the overlaps between identifiers in code and words in natural language descriptions indicate that the code snippet and the description may potentially be related.
Topics & Concepts
Computer scienceCode (set theory)SnippetIdentifierInformation retrievalNatural languageSet (abstract data type)Code reuseSource codeFeature (linguistics)Natural language processingProgramming languageArtificial intelligenceSoftwareLinguisticsPhilosophySoftware Engineering ResearchTopic ModelingSoftware System Performance and Reliability