[Retracted] An Intelligent Code Search Approach Using Hybrid Encoders

Abstract

The intelligent code search with natural language queries has become an important researching area in software engineering. In this paper, we propose a novel deep learning framework At-CodeSM for source code search. The powerful code encoder in At-CodeSM, which is implemented with an abstract syntax tree parsing algorithm (Tree-LSTM) and token-level encoders, maintains both the lexical and structural features of source code in the process of code vectorizing. Both the representative and discriminative models are implemented with deep neural networks. Our experiments on the CodeSearchNet dataset show that At-CodeSM yields better performance in the task of intelligent code searching than previous approaches.

Incorporating Code Structure and Quality in Deep Code Search

“…NCS extracts specific keywords from code snippets and uses only the word embedding mechanism to obtain the vector of code snippets. Yao [12] used Tree LSTM to process the abstract syntax tree of code snippets and proposed a new code search method named At-CodeSM. These methods used different techniques to extract the semantic features of code snippets and completed the code search task by comparing the similarity between vectors of code snippets and natural language queries.…”

Retracted: An Intelligent Code Search Approach Using Hybrid Encoders

“…This article has been retracted by Hindawi, as publisher, following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of systematic manipulation of the publication and peer-review process.…”